ORCID Profile
0000-0002-0608-7288
Current Organisations
Osaka University
,
IPB UNIVERSITY
,
Barcelona Supercomputing Center
,
University of Western Ontario
,
Instituciò Catalana de Recerca i Estudis Avancats
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Atmospheric Sciences | Meteorology | Climatology (excl. Climate Change Processes) | Climate Change Processes
Effects of Climate Change and Variability on Australia (excl. Social Impacts) | Atmospheric Processes and Dynamics | Climate Variability (excl. Social Impacts) |
Publisher: Frontiers Media SA
Date: 04-12-2019
Publisher: Springer Science and Business Media LLC
Date: 28-05-2016
Publisher: Copernicus GmbH
Date: 04-04-2014
Abstract: Abstract. Climate extremes, such as heat waves and heavy precipitation events, have large impacts on ecosystems and societies. Climate models provide useful tools for studying underlying processes and lifying effects associated with extremes. The Australian Community Climate and Earth System Simulator (ACCESS) has recently been coupled to the Community Atmosphere Biosphere Land Exchange (CABLE) model. We examine how this model represents climate extremes derived by the Expert Team on Climate Change Detection and Indices (ETCCDI) and compare them to observational data sets using the AMIP framework. We find that the patterns of extreme indices are generally well represented. Indices based on percentiles are particularly well represented and capture the trends over the last 60 years shown by the observations remarkably well. The diurnal temperature range is underestimated, minimum temperatures (TMIN) during nights are generally too warm and daily maximum temperatures (TMAX) too low in the model. The number of consecutive wet days is overestimated, while consecutive dry days are underestimated. The maximum consecutive 1-day precipitation amount is underestimated on the global scale. Biases in TMIN correlate well with biases in incoming longwave radiation, suggesting a relationship with biases in cloud cover. Biases in TMAX depend on biases in net shortwave radiation as well as evapotranspiration. The regions and season where the bias in evapotranspiration plays a role for the TMAX bias correspond to regions and seasons where soil moisture availability is limited. Our analysis provides the foundation for future experiments that will examine how land-surface processes contribute to these systematic biases in the ACCESS modelling system.
Publisher: Springer Science and Business Media LLC
Date: 04-03-2019
Publisher: American Meteorological Society
Date: 06-01-2023
Abstract: Robust conclusions regarding changes in the temperature distribution rely on the accuracy and reliability of the input datasets used. Differences between methodologies and datasets in previous studies add uncertainty when comparing and quantifying findings. Here, the authors investigate the sensitivity of assessing global and regional temperature variability and extremes over 1980–2014 in gridded datasets of daily temperature anomalies. A gridded in situ–based dataset, Hadley Centre Global Historical Climatology Network–Daily (HadGHCND), is compared against several commonly used reanalysis products by assessing both the entire distribution and the tails of the distribution. Empirical probability distribution functions show sensitivity to the input dataset when estimating aspects such as standard deviation and skewness, with the mean showing robust results for most regions, irrespective of dataset choice. Standard deviation is especially sensitive, with larger disagreements between datasets for some regions more than others, such as Africa and the Mediterranean region, and with larger differences in minimum temperatures compared with maximum temperatures. Estimates of extreme parameters also show sensitivity to dataset choice, particularly in the lower tails and for daily minimum temperature anomalies. Comparing changes in the means and the extremes of the temperature distributions, the cold extremes in the lower tails have been warming at a faster rate than the mean of the entire distribution for much of the Northern Hemisphere extratropics, with warm extremes warming at a faster rate than the mean in some subtropical regions. These documented sensitivities call for caution when assessing changes in temperature variability and extremes, as dataset choice can have substantial effects on results.
Publisher: Springer Science and Business Media LLC
Date: 15-04-2019
DOI: 10.1038/S41467-019-09729-2
Abstract: Climate change is shaping extreme heat and rain. To what degree human activity has increased the risk of high impact events is of high public concern and still heavily debated. Recent studies attributed single extreme events to climate change by comparing climate model experiments where the influence of an external driver can be included or artificially suppressed. Many of these results however did not properly account for model errors in simulating the probabilities of extreme event occurrences. Here we show, exploiting advanced correction techniques from the weather forecasting field, that correcting properly for model probabilities alters the attributable risk of extreme events to climate change. This study illustrates the need to correct for this type of model error in order to provide trustworthy assessments of climate change impacts.
Publisher: American Geophysical Union (AGU)
Date: 07-10-2018
DOI: 10.1029/2018GL079128
Abstract: Climate simulations of future hot extremes exhibit large uncertainties regarding the magnitude of projected warming. We identify two mechanisms that influence how strongly future heat extremes intensify in climate models. First, the magnitude of extreme temperature increases is determined by changes in preceding seasonal precipitation, connected to lified warming via soil moisture decreases. Second, there are large differences in how models respond to moisture variability those with a stronger response under current climate simulate larger future increases in hot extremes. We build on this mechanistic understanding of future uncertainty and develop a novel constraint, the observed precipitation‐hot temperature relationship, focused on the conditions on the actual hottest day, to identify climate models with realistic land‐atmosphere feedbacks on hot extremes. Applying this constraint to the Coupled Model Intercomparison Project Phase 5 ensemble reduces the probability of the largest increases in projected heat extremes, particularly over Europe and North America.
Publisher: Copernicus GmbH
Date: 29-09-2017
Abstract: Abstract. This article extends a previous study Seneviratne et al. (2016) to provide regional analyses of changes in climate extremes as a function of projected changes in global mean temperature. We introduce the DROUGHT-HEAT Regional Climate Atlas, an interactive tool to analyse and display a range of well-established climate extremes and water-cycle indices and their changes as a function of global warming. These projections are based on simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). A selection of ex le results are presented here, but users can visualize specific indices of interest using the online tool. This implementation enables a direct assessment of regional climate changes associated with global mean temperature targets, such as the 2 and 1.5° limits agreed within the 2015 Paris Agreement.
Publisher: Copernicus GmbH
Date: 27-02-2020
Abstract: Abstract. We present a new global land-based daily precipitation dataset from 1950 using an interpolated network of in situ data called Rainfall Estimates on a Gridded Network – REGEN. We merged multiple archives of in situ data including two of the largest archives, the Global Historical Climatology Network – Daily (GHCN-Daily) hosted by National Centres of Environmental Information (NCEI), USA, and one hosted by the Global Precipitation Climatology Centre (GPCC) operated by Deutscher Wetterdienst (DWD). This resulted in an unprecedented station density compared to existing datasets. The station time series were quality-controlled using strict criteria and flagged values were removed. Remaining values were interpolated to create area-average estimates of daily precipitation for global land areas on a 1∘ × 1∘ latitude–longitude resolution. Besides the daily precipitation amounts, fields of standard deviation, kriging error and number of stations are also provided. We also provide a quality mask based on these uncertainty measures. For those interested in a dataset with lower station network variability we also provide a related dataset based on a network of long-term stations which interpolates stations with a record length of at least 40 years. The REGEN datasets are expected to contribute to the advancement of hydrological science and practice by facilitating studies aiming to understand changes and variability in several aspects of daily precipitation distributions, extremes and measures of hydrological intensity. Here we document the development of the dataset and guidelines for best practices for users with regards to the two datasets.
Publisher: IOP Publishing
Date: 10-2017
Publisher: Copernicus GmbH
Date: 11-02-2021
Abstract: Abstract. In this paper, we present and evaluate the skill of an EC-Earth3.3 decadal prediction system contributing to the Decadal Climate Prediction Project – Component A (DCPP-A). This prediction system is capable of skilfully simulating past global mean surface temperature variations at interannual and decadal forecast times as well as the local surface temperature in regions such as the tropical Atlantic, the Indian Ocean and most of the continental areas, although most of the skill comes from the representation of the external radiative forcings. A benefit of initialization in the predictive skill is evident in some areas of the tropical Pacific and North Atlantic oceans in the first forecast years, an added value that is mostly confined to the south-east tropical Pacific and the eastern subpolar North Atlantic at the longest forecast times (6–10 years). The central subpolar North Atlantic shows poor predictive skill and a detrimental effect of initialization that leads to a quick collapse in Labrador Sea convection, followed by a weakening of the Atlantic Meridional Overturning Circulation (AMOC) and excessive local sea ice growth. The shutdown in Labrador Sea convection responds to a gradual increase in the local density stratification in the first years of the forecast, ultimately related to the different paces at which surface and subsurface temperature and salinity drift towards their preferred mean state. This transition happens rapidly at the surface and more slowly in the subsurface, where, by the 10th forecast year, the model is still far from the typical mean states in the corresponding ensemble of historical simulations with EC-Earth3. Thus, our study highlights the Labrador Sea as a region that can be sensitive to full-field initialization and h er the final prediction skill, a problem that can be alleviated by improving the regional model biases through model development and by identifying more optimal initialization strategies.
Publisher: American Geophysical Union (AGU)
Date: 15-03-2019
DOI: 10.1029/2018JD029541
Publisher: IOP Publishing
Date: 05-2014
Publisher: Springer Science and Business Media LLC
Date: 09-11-2020
DOI: 10.1038/S41598-020-75445-3
Abstract: Prolonged high-temperature extreme events in the ocean, marine heatwaves, can have severe and long-lasting impacts on marine ecosystems, fisheries and associated services. This study applies a marine heatwave framework to analyse a global sea surface temperature product and identify the most extreme events, based on their intensity, duration and spatial extent. Many of these events have yet to be described in terms of their physical attributes, generation mechanisms, or ecological impacts. Our synthesis identifies commonalities between marine heatwave characteristics and seasonality, links to the El Niño-Southern Oscillation, triggering processes and impacts on ocean productivity. The most intense events preferentially occur in summer, when climatological oceanic mixed layers are shallow and winds are weak, but at a time preceding climatological maximum sea surface temperatures. Most subtropical extreme marine heatwaves were triggered by persistent atmospheric high-pressure systems and anomalously weak wind speeds, associated with increased insolation, and reduced ocean heat losses. Furthermore, the most extreme events tended to coincide with reduced chlorophyll- a concentration at low and mid-latitudes. Understanding the importance of the oceanic background state, local and remote drivers and the ocean productivity response from past events are critical steps toward improving predictions of future marine heatwaves and their impacts.
Publisher: Copernicus GmbH
Date: 10-02-2020
Abstract: Abstract. Cold extremes are anticipated to warm at a faster rate than both hot extremes and average temperatures for much of the Northern Hemisphere. Anomalously warm cold extremes can affect numerous sectors, including human health, tourism and various ecosystems that are sensitive to cold temperatures. Using a selection of global climate models, this paper explores the accelerated warming of seasonal cold extremes relative to seasonal mean temperatures in the Northern Hemisphere extratropics. The potential driving physical mechanisms are investigated by assessing conditions on or prior to the day when the cold extreme occurs to understand how the different environmental fields are related. During winter, North America, Europe and much of Eurasia show lified warming of cold extremes projected for the late 21st century, compared to the mid-20th century. This is shown to be largely driven by reductions in cold air temperature advection, suggested as a likely consequence of Arctic lification. In spring and autumn, cold extremes are expected to warm faster than average temperatures for most of the Northern Hemisphere mid-latitudes to high latitudes, particularly Alaska, northern Canada and northern Eurasia. In the shoulder seasons, projected decreases in snow cover and associated reductions in surface albedo are suggested as the largest contributor affecting the accelerated rates of warming in cold extremes. The key findings of this study improve our understanding of the environmental conditions that contribute to the accelerated warming of cold extremes relative to mean temperatures.
Publisher: American Meteorological Society
Date: 10-2015
Abstract: The role of land–atmosphere coupling in modulating the impact of land-use change (LUC) on regional climate extremes remains uncertain. Using the Weather and Research Forecasting Model, this study combines the Global Land–Atmosphere Coupling Experiment with regional LUC to assess the combined impact of land–atmosphere coupling and LUC on simulated temperature extremes. The experiment is applied to an ensemble of planetary boundary layer (PBL) and cumulus parameterizations to determine the sensitivity of the results to model physics. Results show a consistent weakening in the soil moisture–maximum temperature coupling strength with LUC irrespective of the model physics. In contrast, temperature extremes show an asymmetric response to LUC dependent on the choice of PBL scheme, which is linked to differences in the parameterization of vertical transport. This influences convective precipitation, contributing a positive feedback on soil moisture and consequently on the partitioning of the surface turbulent fluxes. The results suggest that the impact of LUC on temperature extremes depends on the land–atmosphere coupling that in turn depends on the choice of PBL. Indeed, the sign of the temperature change in hot extremes resulting from LUC can be changed simply by altering the choice of PBL. The authors also note concerns over the metrics used to measure coupling strength that reflect changes in variance but may not respond to LUC-type perturbations.
Publisher: Frontiers Media SA
Date: 09-06-2021
DOI: 10.3389/FCLIM.2021.678109
Abstract: Observations facilitate model evaluation and provide constraints that are relevant to future predictions and projections. Constraints for uninitialized projections are generally based on model performance in simulating climatology and climate change. For initialized predictions, skill scores over the hindcast period provide insight into the relative performance of models, and the value of initialization as compared to projections. Predictions and projections combined can, in principle, provide seamless decadal to multi-decadal climate information. For that, though, the role of observations in skill estimates and constraints needs to be understood in order to use both consistently across the prediction and projection time horizons. This paper discusses the challenges in doing so, illustrated by ex les of state-of-the-art methods for predicting and projecting changes in European climate. It discusses constraints across prediction and projection methods, their interpretation, and the metrics that drive them such as process accuracy, accurate trends or high signal-to-noise ratio. We also discuss the potential to combine constraints to arrive at more reliable climate prediction systems from years to decades. To illustrate constraints on projections, we discuss their use in the UK's climate prediction system UKCP18, the case of model performance weights obtained from the Climate model Weighting by Independence and Performance (ClimWIP) method, and the estimated magnitude of the forced signal in observations from detection and attribution. For initialized predictions, skill scores are used to evaluate which models perform well, what might contribute to this performance, and how skill may vary over time. Skill estimates also vary with different phases of climate variability and climatic conditions, and are influenced by the presence of external forcing. This complicates the systematic use of observational constraints. Furthermore, we illustrate that sub-selecting simulations from large ensembles based on reproduction of the observed evolution of climate variations is a good testbed for combining projections and predictions. Finally, the methods described in this paper potentially add value to projections and predictions for users, but must be used with caution.
Publisher: Springer Science and Business Media LLC
Date: 07-03-2016
DOI: 10.1038/NCLIMATE2941
Publisher: IOP Publishing
Date: 23-04-2020
Abstract: A range of in situ , satellite and reanalysis products on a common daily 1° × 1° latitude/longitude grid were extracted from the Frequent Rainfall Observations on Grids database to help facilitate intercomparison and analysis of precipitation extremes on a global scale. 22 products met the criteria for this analysis, namely that daily data were available over global land areas from 50°S to 50°N since at least 2001. From these daily gridded data, 10 annual indices that represent aspects of extreme precipitation frequency, duration and intensity were calculated. Results were analysed for in idual products and also for four cluster types: (i) in situ , (ii) corrected satellite, (iii) uncorrected satellite and (iv) reanalyses. Climatologies based on a common 13-year period (2001–2013) showed substantial differences between some products. Timeseries (which ranged from 13 years to 67 years) also highlighted some substantial differences between products. A coefficient of variation showed that the in situ products were most similar to each other while reanalysis products had the largest variations. Reanalyses however agreed better with in situ observations over extra-tropical land areas compared to the satellite clusters, although reanalysis products tended to fall into ‘wet’ and ‘dry’ c s overall. Some indices were more robust than others across products with daily precipitation intensity showing the least variation between products and days above 20 mm showing the largest variation. In general, the results of this study show that global space-based precipitation products show the potential for climate scale analyses of extremes. While we recommend caution for all products dependent on their intended application, this particularly applies to reanalyses which show the most ergence across results.
Publisher: Springer Science and Business Media LLC
Date: 26-02-2014
DOI: 10.1038/NCLIMATE2145
Publisher: American Geophysical Union (AGU)
Date: 21-07-2022
DOI: 10.1029/2022JD036673
Abstract: Weather regimes are large‐scale atmospheric circulation states that frequently occur in the climate system with persistence and recurrence, and are associated with the occurrence of specific local weather conditions. This study evaluates the representation of the four Euro‐Atlantic weather regimes in uninitialized historical forcing simulations and initialized decadal predictions performed with the EC‐Earth3 coupled climate model. The four weather regimes are the positive and negative phases of the North Atlantic Oscillation (NAO+ and NAO−, respectively), Blocking, and Atlantic Ridge in winter and the NAO−, Blocking, Atlantic Ridge, and Atlantic Low in summer. We also analyze the impact that the model initialization toward the observed state of the climate system has on the ability to predict the variability of the weather regimes' seasonal frequency of occurrence. We find that the EC‐Earth3 model correctly reproduces the spatial patterns and climatological occurrence frequencies of the four weather regimes. By contrast, the skill in predicting the inter‐annual to decadal variations of the weather regimes' seasonal frequencies is generally low, and the initialization does not significantly improve such skill. The observed teleconnections between the weather regimes and the North Atlantic sea surface temperatures are generally not reproduced by the model, which could be a reason for the low skill in predicting the temporal variations of the weather regime frequencies.
Publisher: IOP Publishing
Date: 06-12-2019
Publisher: Springer Science and Business Media LLC
Date: 11-10-2023
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-13156
Abstract: & & Decadal climate predictions are a new source of climate information for inter-annual to decadal time scales, which is of increasing interest for users. Forecast quality assessment is essential to identify windows of opportunity (e.g., variables, regions, and lead times) with skill that can be used to develop a climate service and inform users in several sectors. Also, it can help to monitor improvements in current forecast systems. The Decadal Climate Prediction Project Component A (DCPP-A) of the Coupled Model Intercom-parison Project Phase 6 (CMIP6) now provides the most comprehensive set of retrospective decadal predictions from multiple forecast systems. The increasing availability of these simulations leads to the question of how to best post-process the raw output from the forecast systems so that the most useful and reliable information is provided to users.& & & & This work evaluates the quality of deterministic and probabilistic forecasts for spatial fields of near-surface air temperature and precipitation, and time series of the Atlantic multi-decadal variability index (AMV) and global near-surface air temperature anomalies (GSAT) generated from all the available decadal predictions contributing to CMIP6/DCPP-A (169 members from 13 forecast systems). The predictions generally show high skill in predicting temperature and the AMV and GSAT time series, while the skill is more limited for precipitation. Also, different approaches for building a multi-model forecast are compared (pooling all ensemble members versus combining the averages from in idual forecast systems), finding small differences. Besides, the multi-model ensemble is compared to the in idual forecast systems. The best system usually provides the highest skill. However, the multi-model ensemble is a reasonable choice for not having to select the best system for each particular variable, forecast period and region. Furthermore, the decadal predictions are compared to the uninitialized historical climate simulations (195 members from the same forecast systems as the decadal prediction members) to estimate the impact of initialization. An added value is found for temperature over several ocean and land regions, and for the AMV and GSAT time series, while it is more reduced for precipitation. Moreover, the full DCPP-A ensemble is compared to a sub-ensemble of predictions that could be provided in near real-time for a potential operational product generation. The comparison shows a benefit of using a large ensemble over several regions, especially for temperature. Finally, the implications of these results in a climate services context are discussed.& & & & & & / &
Publisher: Wiley
Date: 06-05-2013
DOI: 10.1002/JOC.3707
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-13507
Abstract: Evaluating the trends of extreme indices (EI) is crucial to detect and attribute extreme events (EE) and establish adaptation and mitigation strategies to the current and future climate conditions. However, the observational climate data used for the calculation of these indices often contains many missing values and leads to incomplete and inaccurate EI. This problem is even greater as we go back in time due to the scarcity of the older measurements.To tackle this problem, interpolation techniques such as the kriging method are often used to fill in the gaps. However, it has been shown that such techniques are inadequate to reconstruct specific climatic patterns [1]. Deep-learning based technologies give the possibility to surpass standard statistical methods by learning complex patterns and features in climate data.In this work, we are using an inpainting technique based on a U-Net neural network made of partial convolutional layers and a loss function designed to produce semantically meaningful predictions [1]. Models are trained using vast amounts of climate model data and can be used to reconstruct large and irregular regions of missing data with few computational resources.The efficiency of the method is well demonstrated through its application to the HadEX3 dataset [2]. This dataset contains gridded land surface EI, among which the TX90p index that measures the monthly (or annual) frequency of warm days (defined as a percentage of days where daily maximum temperature is above the 90th percentile). As for other EI, there is a lack of TX90p values in many regions of the world, even in recent years. It is particularly true when looking at an intermediate product of HadEX3 where the station-based indices have been combined without interpolation. This is illustrated by the left map of the figure where the gray pixels correspond to missing values. By training our model using data from the CMIP6 archive, we have been able to reconstruct the missing TX90p values for all the time steps of HadEX3 (see right map in the figure) and detect EE that were not included in the original dataset. The reconstructed dataset is being prepared for the community in the framework of the H2020 CLINT project [3] for further detection and attribution studies.[1] Kadow C. et al., Nat. Geosci., 13, 408-413 (2020)[2] Dunn R.J.H. et al., J. Geophys. Res. Atmos., 125, 1 (2020)[3] climateintelligence.eu/
Publisher: Springer Science and Business Media LLC
Date: 06-05-2015
Publisher: Springer Science and Business Media LLC
Date: 10-04-2018
DOI: 10.1038/S41467-018-03732-9
Abstract: Heatwaves are important climatic extremes in atmospheric and oceanic systems that can have devastating and long-term impacts on ecosystems, with subsequent socioeconomic consequences. Recent prominent marine heatwaves have attracted considerable scientific and public interest. Despite this, a comprehensive assessment of how these ocean temperature extremes have been changing globally is missing. Using a range of ocean temperature data including global records of daily satellite observations, daily in situ measurements and gridded monthly in situ-based data sets, we identify significant increases in marine heatwaves over the past century. We find that from 1925 to 2016, global average marine heatwave frequency and duration increased by 34% and 17%, respectively, resulting in a 54% increase in annual marine heatwave days globally. Importantly, these trends can largely be explained by increases in mean ocean temperatures, suggesting that we can expect further increases in marine heatwave days under continued global warming.
Publisher: IOP Publishing
Date: 14-02-2023
Abstract: The occurrence of extreme climate events in the coming years is modulated by both global warming and internal climate variability. Anticipating changes in frequency and intensity of such events in advance may help minimize the impact on climate-vulnerable sectors and society. Decadal climate predictions have been developed as a source of climate information relevant for decision-making at multi-annual timescales. We evaluate the multi-model forecast quality of the CMIP6 decadal hindcasts in predicting a set of indices measuring different characteristics of temperature and precipitation extremes for the forecast years 1-5. The multi-model ensemble skillfully predicts the temperature extremes over most land regions, while the skill is more limited for precipitation extremes. We further compare the prediction skill for these extreme indices to the skill for mean temperature and precipitation, finding that the extreme indices are predicted with lower skill, particularly those representing the most extreme days. We find only small and region-dependent improvements from model initialization in comparison to historical forcing simulations. This systematic evaluation of decadal hindcasts is essential when providing a climate service based on decadal predictions so that the user is informed on the trustworthiness of the forecasts for each specific region and extreme event.
Publisher: Copernicus GmbH
Date: 19-10-2022
Abstract: Abstract. Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure, and precipitation on decadal to multi-decadal timescales. We find that the constrained projections show significant skill in predicting the climate of the following 10 to 20 years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first 2 decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the ex le of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades.
Publisher: American Meteorological Society
Date: 03-11-2016
Abstract: The skill of eight climate models in simulating the variability and trends in the observed areal extent of daily temperature and precipitation extremes is evaluated across five large-scale regions, using the climate extremes index (CEI) framework. Focusing on Europe, North America, Asia, Australia, and the Northern Hemisphere, results show that overall the models are generally able to simulate the decadal variability and trends of the observed temperature and precipitation components over the period 1951–2005. Climate models are able to reproduce observed increasing trends in the area experiencing warm maximum and minimum temperature extremes, as well as, to a lesser extent, increasing trends in the areas experiencing an extreme contribution of heavy precipitation to total annual precipitation for the Northern Hemisphere regions. Using simulations performed under different radiative forcing scenarios, the causes of simulated and observed trends are investigated. A clear anthropogenic signal is found in the trends in the maximum and minimum temperature components for all regions. In North America, a strong anthropogenically forced trend in the maximum temperature component is simulated despite no significant trend in the gridded observations, although a trend is detected in a reanalysis product. A distinct anthropogenic influence is also found for trends in the area affected by a much-above-average contribution of heavy precipitation to annual precipitation totals for Europe in a majority of models and to varying degrees in other Northern Hemisphere regions. However, observed trends in the area experiencing extreme total annual precipitation and extreme number of wet and dry days are not reproduced by climate models under any forcing scenario.
Publisher: Wiley
Date: 10-06-2019
DOI: 10.1002/JOC.6138
Publisher: IEEE
Date: 16-09-2020
Publisher: American Meteorological Society
Date: 29-07-2014
DOI: 10.1175/JCLI-D-13-00715.1
Abstract: Leading patterns of observed monthly extreme rainfall variability in Australia are examined using an empirical orthogonal teleconnection (EOT) method. Extreme rainfall variability is more closely related to mean rainfall variability during austral summer than in winter. The leading EOT patterns of extreme rainfall explain less variance in Australia-wide extreme rainfall than is the case for mean rainfall EOTs. The authors illustrate that, as with mean rainfall, the El Niño–Southern Oscillation (ENSO) has the strongest association with warm-season extreme rainfall variability, while in the cool season the primary drivers are atmospheric blocking and the subtropical ridge. The Indian Ocean dipole and southern annular mode also have significant relationships with patterns of variability during austral winter and spring. Leading patterns of summer extreme rainfall variability have predictability several months ahead from Pacific sea surface temperatures (SSTs) and as much as a year in advance from Indian Ocean SSTs. Predictability from the Pacific is greater for wetter-than-average summer months than for months that are drier than average, whereas for the Indian Ocean the relationship has greater linearity. Several cool-season EOTs are associated with midlatitude synoptic-scale patterns along the south and east coasts. These patterns have common atmospheric signatures denoting moist onshore flow and strong cyclonic anomalies often to the north of a blocking anticyclone. Tropical cyclone activity is observed to have significant relationships with some warm-season EOTs. This analysis shows that extreme rainfall variability in Australia can be related to remote drivers and local synoptic-scale patterns throughout the year.
Publisher: American Geophysical Union (AGU)
Date: 18-07-2023
DOI: 10.1029/2022GL102466
Abstract: Future precipitation changes are typically estimated from climate model simulations, while the credibility of such projections needs to be assessed by their ability to capture observed precipitation changes. Here we evaluate how skillfully historical climate simulations contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) capture observed changes in mean and extreme precipitation. We find that CMIP6 historical simulations skillfully represent observed precipitation changes over large parts of Europe, Asia, northeastern North America, parts of South America and western Australia, whereas a lack of skill is apparent in western North America and parts of Africa. In particular in regions with moderate skill the availability of very large ensembles can be beneficial to improve the simulation accuracy. CMIP6 simulations are regionally skillful where they capture observed (positive or negative) trends, whereas a lack of skill is found in regions characterized by negative observed precipitation trends where CMIP6 simulates increases.
Publisher: Springer Science and Business Media LLC
Date: 08-04-2015
Publisher: Hindawi Limited
Date: 2015
DOI: 10.1155/2015/325718
Abstract: Daily gridded precipitation data are needed for investigating spatiotemporal variability of precipitation, including extremes however, uncertainties related to daily precipitation products are large. Here, we compare a range of precipitation grids for Australia. These datasets include products derived solely from in situ observations (interpolated datasets) and two products that combine both remote sensed data and in situ observations. We find that all precipitation grids have similar climatologies for annual aggregated precipitation totals and annual maximum precipitation. The temporal correlations of daily precipitation values are higher between the interpolated datasets, but the correlations between the most widely used interpolated product (AWAP) and the two remotely sensed products (TRMM and GPCP) are still reasonable. Our results, however, point to distinct structural uncertainties between those datasets gridding in situ observations and those datasets deriving precipitation estimates primarily from satellite measurements. All datasets analysed agree well for low to moderate daily precipitation amounts up to about 20 mm but erge at upper quantiles, indicating that substantial uncertainty exists in gridded precipitation extremes over Australia.
Publisher: Springer Science and Business Media LLC
Date: 29-01-2018
Publisher: Copernicus GmbH
Date: 26-11-2012
Abstract: Abstract. The impact of historical land use induced land cover change (LULCC) on regional-scale climate extremes is examined using four climate models within the Land Use and Climate, IDentification of robust impacts project. To assess those impacts, multiple indices based on daily maximum and minimum temperatures and daily precipitation were used. We contrast the impact of LULCC on extremes with the impact of an increase in atmospheric CO2 from 280 ppmv to 375 ppmv. In general, consistent changes in both high and low temperature extremes are similar to the simulated change in mean temperature caused by LULCC and are restricted to regions of intense modification. The impact of LULCC on both means and on most temperature extremes is statistically significant. While the magnitude of the LULCC-induced change in the extremes can be of similar magnitude to the response to the change in CO2, the impacts of LULCC are much more geographically isolated. For most models, the impacts of LULCC oppose the impact of the increase in CO2 except for one model where the CO2-caused changes in the extremes are lified. While we find some evidence that in idual models respond consistently to LULCC in the simulation of changes in rainfall and rainfall extremes, LULCC's role in affecting rainfall is much less clear and less commonly statistically significant, with the exception of a consistent impact over South East Asia. Since the simulated response of mean and extreme temperatures to LULCC is relatively large, we conclude that unless this forcing is included, we risk erroneous conclusions regarding the drivers of temperature changes over regions of intense LULCC.
Publisher: Elsevier BV
Date: 09-2016
Publisher: IOP Publishing
Date: 05-2019
Abstract: Climate extremes, such as droughts or heat waves, can lead to harvest failures and threaten the livelihoods of agricultural producers and the food security of communities worldwide. Improving our understanding of their impacts on crop yields is crucial to enhance the resilience of the global food system. This study analyses, to our knowledge for the first time, the impacts of climate extremes on yield anomalies of maize, soybeans, rice and spring wheat at the global scale using sub-national yield data and applying a machine-learning algorithm. We find that growing season climate factors—including mean climate as well as climate extremes—explain 20%–49% of the variance of yield anomalies (the range describes the differences between crop types), with 18%–43% of the explained variance attributable to climate extremes, depending on crop type. Temperature-related extremes show a stronger association with yield anomalies than precipitation-related factors, while irrigation partly mitigates negative effects of high temperature extremes. We developed a composite indicator to identify hotspot regions that are critical for global production and particularly susceptible to the effects of climate extremes. These regions include North America for maize, spring wheat and soy production, Asia in the case of maize and rice production as well as Europe for spring wheat production. Our study highlights the importance of considering climate extremes for agricultural predictions and adaptation planning and provides an overview of critical regions that are most susceptible to variations in growing season climate and climate extremes.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-7894
Abstract: The Mediterranean region is often considered a climate change hotspot. State of the art climate projections display a large uncertainty due to factors such as the modeling approach or variability phasing, in particular for projections of the next few decades. With the great amount of climate information from the last Coupled Model Intercomparison Project (CMIP6), it is desirable to constrain the multi-model multi-member ensemble for the near-term future to obtain more robust and better performing projections. We explore different subsetting methods that select those members that better capture the variability at the starting date of the projection& #8217 s study period. To find the best information we explore variations of different parameters in the selection method based on relevant climate drivers in the Mediterranean region. Such parameters are the reference against which the best members are selected, and comprise: the time period used, the constraining regions considered and the variable or metric that drive the constraint. We find that these subsetting methods can improve the accuracy of near-term climate projections for the next 20 years compared to the unconstrained projections. This evaluation of the results allows us to make informed and robust decisions about the near-term future projections based on quality estimates borrowed from climate prediction practices.
Publisher: Copernicus GmbH
Date: 12-05-2011
DOI: 10.5194/NHESS-11-1351-2011
Abstract: Abstract. Extreme wind speeds and related storm loss potential in Europe have been investigated using multi-model simulations from global (GCM) and regional (RCM) climate models. Potential future changes due to anthropogenic climate change have been analysed from these simulations following the IPCC SRES A1B scenario. The large number of available simulations allows an estimation of the robustness of detected future changes. All the climate models reproduced the observed spatial patterns of wind speeds, although some models displayed systematic biases. A storm loss model was applied to the GCM and RCM simulated wind speeds, resulting in realistic mean loss amounts calculated from 20th century climate simulations, although the inter-annual variability of losses is generally underestimated. In future climate simulations, enhanced extreme wind speeds were found over northern parts of Central and Western Europe in most simulations and in the ensemble mean (up to 5%). As a consequence, the loss potential is also higher in these regions, particularly in Central Europe. Conversely, a decrease in extreme wind speeds was found in Southern Europe, as was an associated reduction in loss potential. There was considerable spread in the projected changes of in idual ensemble members, with some indicating an opposite signature to the ensemble mean. Downscaling of the large-scale simulations with RCMs has been shown to be an important source of uncertainty. Even RCMs with identical boundary forcings can show a wide range of potential changes. The robustness of the projected changes was estimated using two different measures. First, the inter-model standard deviation was calculated however, it is sensitive to outliers and thus displayed large uncertainty ranges. Second, a multi-model combinatorics approach considered all possible sub-ensembles from GCMs and RCMs, hence taking into account the arbitrariness of model selection for multi-model studies. Based on all available GCM and RCM simulations, for ex le, a 25% mean increase in risk of loss for Germany has been estimated for the end of the 21st century, with a 90% confidence range of +15 to +35%.
Publisher: American Meteorological Society
Date: 2021
Abstract: Decadal climate predictions are being increasingly used by stakeholders interested in the evolution of climate over the coming decade. However, investigating the added value of those initialized decadal predictions over other sources of information typically used by stakeholders generally relies on forecast accuracy, while probabilistic aspects, although crucial to users, are often overlooked. In this study, the quality of the near-surface air temperature from initialized predictions has been assessed in terms of reliability, an essential characteristic of climate simulation ensembles, and compared to the reliability of noninitialized simulations performed with the same model ensembles. Here, reliability is defined as the capability to obtain a true estimate of the forecast uncertainty from the ensemble spread. We show the limited added value of initialization in terms of reliability, the initialized predictions being significantly more reliable than their noninitialized counterparts only for specific regions and the first forecast year. By analyzing reliability for different forecast system ensembles, we further highlight the fact that the combination of models seems to play a more important role than the ensemble size of each in idual forecast system. This is due to s ling different model errors related to model physics, numerics, and initialization approaches involved in the multimodel, allowing for a certain level of error compensation. Finally, this study demonstrates that all forecast system ensembles are affected by systematic biases and dispersion errors that affect the reliability. This set of errors makes bias correction and calibration necessary to obtain reliable estimates of forecast probabilities that can be useful to stakeholders.
Publisher: Inter-Research Science Center
Date: 20-05-2010
DOI: 10.3354/CR00853
Publisher: Springer Science and Business Media LLC
Date: 26-03-2019
Publisher: IOP Publishing
Date: 31-05-2019
Abstract: Changes in precipitation totals and extremes are among the most relevant consequences of climate change, but in particular regional changes remain uncertain. While aggregating over larger regions reduces the noise in time series and typically shows increases in the intensity of precipitation extremes, it has been argued that this may not be the case in water-limited regions. Here we investigate long-term changes in annual precipitation totals and extremes aggregated over the world’s humid, transitional, and dry regions as defined by their climatological water availability. We use the globally most complete observational datasets suitable for the analysis of daily precipitation extremes, and data from global climate model simulations. We show that precipitation totals and extremes have increased in the humid regions since the mid-20th century. Conversely, despite showing tendencies to increase, no robust changes can be detected in the drier regions, in part due to the large variability of precipitation and sparse observational coverage particularly in the driest regions. Future climate simulations under increased radiative forcing indicate total precipitation increases in more humid regions but no clear changes in the more arid regions, while precipitation extremes are more likely to increase than to decrease on average over both the humid and arid regions of the world. These results highlight the increasing risk of heavy precipitation in most regions of the world, including water-limited regions, with implications for related impacts through flooding risk or soil erosion.
Publisher: American Geophysical Union (AGU)
Date: 07-06-2019
DOI: 10.1029/2019GL081898
Publisher: IOP Publishing
Date: 10-2017
Publisher: Copernicus GmbH
Date: 26-05-2023
DOI: 10.5194/EGUSPHERE-2023-962
Abstract: Abstract. Previous studies agree on an impact of the Atlantic Multidecadal Variability (AMV) on total seasonal rainfall amounts over the Sahel. However, whether and how AMV affects the distribution of rainfall or the timing of the West African Monsoon is not well known. Here we analyze daily rainfall outputs from atmosphere-ocean coupled models. Models show dry biases over the Sahel, where the mean intensity is consistently smaller than observations, and wet biases over the Guinea Coast, where they simulate too many rainy days. In addition, most models underestimate the average length of the rainy season over the Sahel, some due to a too late monsoon onset and others due to a too early cessation. In response to a persistent positive AMV pattern imposed in the Atlantic, following a protocol largely consistent with the one proposed by the Component C of the Decadal Climate Prediction Project (DCPP-C), models show an enhancement in total summer rainfall over West African land mass, including the Sahel. Both the number of wet days and the intensity of daily rainfall events are enhanced over the Sahel. The former explains most of the changes in seasonal rainfall in the northern fringe, while the latter is more relevant in the southern region, where higher rainfall anomalies occur. This dominance is connected to the changes in the number of days per type of event: the frequency of both moderate and heavy events increases over the Sahel’s northern fringe. Conversely, over the southern limit, it is mostly the frequency of heavy events which is enhanced, affecting the mean rainfall intensity there. Extreme rainfall events are also enhanced over the whole Sahel in response to a positive phase of the AMV. Models with stronger negative biases in rainfall amounts tend to show weaker changes in response to AMV, suggesting systematic biases could affect the simulated responses. The monsoon onset over the Sahel shows no clear response to AMV, while the demise tends to be delayed and the overall length of the monsoon season enhanced between 2 and 5 days with the positive AMV pattern. The effect of AMV on the seasonality of the monsoon is more consistent to the West of 10º W, with all models showing a statistically significant earlier onset, later demise and enhanced monsoon season with the positive phase of the AMV. Our results suggest a potential for the decadal prediction of changes in the intraseasonal characteristics of rainfall over the Sahel, including the occurrence of extreme events.
Publisher: Elsevier BV
Date: 09-2018
Publisher: Copernicus GmbH
Date: 23-12-2021
Publisher: Springer Science and Business Media LLC
Date: 14-06-2019
DOI: 10.1038/S41467-019-10206-Z
Abstract: Marine heatwaves (MHWs) can cause devastating impacts to marine life. Despite the serious consequences of MHWs, our understanding of their drivers is largely based on isolated case studies rather than any systematic unifying assessment. Here we provide the first global assessment under a consistent framework by combining a confidence assessment of the historical refereed literature from 1950 to February 2016, together with the analysis of MHWs determined from daily satellite sea surface temperatures from 1982–2016, to identify the important local processes, large-scale climate modes and teleconnections that are associated with MHWs regionally. Clear patterns emerge, including coherent relationships between enhanced or suppressed MHW occurrences with the dominant climate modes across most regions of the globe – an important exception being western boundary current regions where reports of MHW events are few and ocean-climate relationships are complex. These results provide a global baseline for future MHW process and prediction studies.
Publisher: American Meteorological Society
Date: 07-2013
Publisher: Copernicus GmbH
Date: 09-11-2022
DOI: 10.5194/HESS-26-5605-2022
Abstract: Abstract. Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analysed, and the result of a research experiment is presented using model weighting with the participation of six climate model experts and six hydrological model experts. For the experiment, seven climate models are a priori selected from a larger EURO-CORDEX (Coordinated Regional Downscaling Experiment – European Domain) ensemble of climate models, and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two in idual rounds of elicitation of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological-impact modellers in general are more open for assigning weights to different models in a multi-model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only re-establish a uniform weight between climate models.
Publisher: American Geophysical Union (AGU)
Date: 12-06-2018
DOI: 10.1029/2018JD028549
Publisher: Copernicus GmbH
Date: 23-12-2021
Abstract: Abstract. Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analyzed and the result of a research experiment is presented using model weighting with the participation of 6 climate model experts and 6 hydrological model experts. For the experiment, seven climate models are a-priori selected from a larger Euro-CORDEX ensemble of climate models and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two in idual elicitations of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological impact modellers in general are more open for assigning weights to different models in a multi model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models, and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only reestablish a uniform weight between climate models.
Publisher: American Meteorological Society
Date: 11-2020
Publisher: American Meteorological Society
Date: 07-2015
DOI: 10.1175/2015BAMSSTATEOFTHECLIMATE.1
Abstract: Editors note: For easy download the posted pdf of the State of the Climate for 2014 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.
Publisher: Copernicus GmbH
Date: 12-12-2014
Abstract: Abstract. We assess the effects of different methodological choices made during the construction of gridded data sets of climate extremes, focusing primarily on HadEX2. Using global land-surface time series of the indices and their coverage, as well as uncertainty maps, we show that the choices which have the greatest effect are those relating to the station network used or that drastically change the values for in idual grid boxes. The latter are most affected by the number of stations required in or around a grid box and the gridding method used. Most parametric changes have a small impact, on global and on grid box scales, whereas structural changes to the methods or input station networks may have large effects. On grid box scales, trends in temperature indices are very robust to most choices, especially in areas which have high station density (e.g. North America, Europe and Asia). The precipitation indices, being less spatially correlated, can be more susceptible to methodological choices, but coherent changes are still clear in regions of high station density. Regional trends from all indices derived from areas with few stations should be treated with care. On a global scale, the linear trends over 1951–2010 from almost all choices fall within the 5–95th percentile range of trends from HadEX2. This demonstrates the robust nature of HadEX2 and related data sets to choices in the creation method.
Publisher: American Meteorological Society
Date: 07-2014
DOI: 10.1175/JCLI-D-13-00405.1
Abstract: Changes in climate extremes are often monitored using global gridded datasets of climate extremes based on in situ observations or reanalysis data. This study assesses the consistency of temperature and precipitation extremes between these datasets. Both the temporal evolution and spatial patterns of annual extremes of daily values are compared across multiple global gridded datasets of in situ observations and reanalyses to make inferences on the robustness of the obtained results. While normalized time series generally compare well, the actual values of annual extremes of daily data differ systematically across the different datasets. This is partly related to different computational approaches when calculating the gridded fields of climate extremes. There is strong agreement between extreme temperatures in the different in situ–based datasets. Larger differences are found for temperature extremes from the reanalyses, particularly during the presatellite era, indicating that reanalyses are most consistent with purely observational-based analyses of changes in climate extremes for the three most recent decades. In terms of both temporal and spatial correlations, the ECMWF reanalyses tend to show greater agreement with the gridded in situ–based datasets than the NCEP reanalyses and Japanese 25-year Reanalysis Project (JRA-25). Extreme precipitation is characterized by higher temporal and spatial variability than extreme temperatures, and there is less agreement between different datasets than for temperature. However, reasonable agreement between the gridded observational precipitation datasets remains. Extreme precipitation patterns and time series from reanalyses show lower agreement but generally still correlate significantly.
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-6141
Abstract: & & Marine heat waves (MHWs) and cold spells (MCSs) are anomalous ocean temperature events that occur in all oceans and seas with great ecological and economic impacts. The quantification of the relative importance of marine temperature extreme events is often done through the calculation of local metrics, the majority of them not considering explicitly the spatial extent of the events. Here, we propose a ranking methodology to evaluate the relative importance of marine temperature extreme events between 1982 and 2021 within the Mediterranean basin. We introduce a metric, generically termed activity, combining the number of events, duration, intensity and spatial extent of: i) summer MHWs and ii) winter MCSs. Results at the entire Mediterranean scale show that the former dominate in the last two decades while the latter are prevalent in the 1980s and 1990s. Summers with the highest MHW activity were 2018, 2003 and 2015 and winters with the strongest MCS activity took place in 1992, 1984 and 1983. The highest MHW activity occurred in the Gulf of Lion while the highest MCS activity took place preferably in the Aegean basin. According to our proposed definition, the three strongest MHWs almost double the duration, mean intensity, and activity of the three strongest MCSs. The long-term tendency of activity shows a rapid increase for summer MHWs and a linear decrease for winter MCSs in the Mediterranean over the last four decades.& & & & & & & & & & span& & span& We acknowledge the financing support from FCT & #8211 JPIOCEANS/0001/2019& /span& & /span& & &
Publisher: IOP Publishing
Date: 09-2015
Publisher: American Geophysical Union (AGU)
Date: 07-2011
DOI: 10.1029/2011GL047995
Publisher: American Geophysical Union (AGU)
Date: 11-12-2021
DOI: 10.1029/2021GL094915
Abstract: Targeted adaptation to near‐term climate change requires accurate, reliable, and actionable climate information for the next few decades. Climate projections simulate the response to radiative forcing, but are subject to substantial uncertainties due to internal variability. Decadal climate predictions aim to reduce this uncertainty by initializing the simulations using observations, but are typically limited to the next 10 years. Here, we use decadal predictions to constrain climate projections beyond the next decade and demonstrate that accounting for climate variability improves regional projections of 20‐year average temperatures. Applying this constraint to climate projections of the near future until 2035, summer temperatures over land regions in Asia and Africa tend to show stronger changes within the warming range simulated by the larger, unconstrained, ensemble—consistent with a warm phase in North Atlantic variability. This improved regional climate information can enable tailored adaptation to climate changes in the coming decades.
Publisher: Wiley
Date: 12-02-2014
DOI: 10.1002/JOC.3917
Publisher: American Geophysical Union (AGU)
Date: 28-08-2018
DOI: 10.1029/2018GL078875
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-9418
Abstract: & & The increasing risk of dry extremes and droughts and their further projected exacerbation due to climate change urges the development of reliable risk assessments and mitigation pathways on a regional and global scale. This foremost requires accurate and unambiguous model predictions of dry extremes, as this underpins the effectiveness of the proposed strategies. At present, however, the confidence in regional drought projections is defined as & #8216 medium to low' by the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report (AR6), and reducing this uncertainty remains one of the main goals in coming years. & br& In this study, the bias in future projected changes in annual meteorological drought duration (hereafter, longest annual drought, LAD) is assessed in the ensemble of CMIP5 and CMIP6 models. The analyses show that it is the present-day inter-model spread in LAD climatology that largely determines the inter-model uncertainty in future predicted LAD changes. Hereby, both CMIP5 and CMIP6 model ensembles indicate a robust & #8220 dry-model-gets-drier& #8221 relationship in future LAD projections on a global and regional scale. Correcting for this bias using emerging constraint principles and past observational LAD information, we find that nearly half of the world's land area with projected increases in drought duration is underestimating the predicted model ensemble mean change, imposing higher-than-expected risks to the societies and ecosystems. Analysis of physical mechanisms that could underlie this emergent & #8220 resent-future relationship& #8221 points to differences in the responses of & #8220 dry models& #8221 and & #8220 wet models& #8221 to CO2 forcing. Dry and wet models show differences in climate states, which support the role of land& #8211 atmosphere feedbacks and convective scheme sensitivity to atmospheric moisture in the spread of future LAD change projections. & br& In conclusion, the study reveals world regions where climate change may cause stronger drought duration aggravation than expected, and emphasizes the importance of reducing systematic model errors, which are presently largely owed to rainfall biases. Correcting these biases will increase the confidence of future dry extremes predictions, a prerequisite for the effective drought risk reduction in the near future with direct benefits for human and natural systems.& &
Publisher: Elsevier
Date: 2019
Publisher: Elsevier BV
Date: 02-2016
Publisher: American Geophysical Union (AGU)
Date: 15-07-2017
DOI: 10.1002/2017GL073733
Publisher: Research Square Platform LLC
Date: 08-06-2023
DOI: 10.21203/RS.3.RS-3009767/V1
Abstract: Numerical Earth System Models (ESMs) are our best tool to predict the evolution of atmospheric CO2 concentration and its effect on Global temperature. However, large uncertainties exist among ESMs in the year-to-year variations of atmospheric CO2 concentration. This prevents us from precisely understanding its past evolution and from accurately estimating its future evolution. Here we analyze various ESMs simulations from the 6th Coupled Model Intercomparison Projects (CMIP6) to understand the origins of the inter-model uncertainty in the interannual variability of the atmospheric CO2 concentration. We show that most of this uncertainty is coming from the simulation of the land CO2 flux internal variability. Although models agree that those variations are driven by El Niño Southern Oscillation (ENSO), similar ENSO-related surface temperature and precipitation teleconnections across models drive different land CO2 fluxes, pointing to the land vegetation models as the dominant source of the inter-model uncertainty.
Publisher: American Meteorological Society
Date: 07-2014
Publisher: Elsevier BV
Date: 09-2015
Publisher: American Geophysical Union (AGU)
Date: 28-07-2012
DOI: 10.1029/2012GL052459
Publisher: American Geophysical Union (AGU)
Date: 23-05-2013
DOI: 10.1002/GRL.50427
Publisher: American Geophysical Union (AGU)
Date: 25-02-2012
DOI: 10.1029/2011JD016382
Publisher: Springer Science and Business Media LLC
Date: 26-11-2013
DOI: 10.1038/NCLIMATE2061
Publisher: Copernicus GmbH
Date: 11-04-2022
Publisher: American Meteorological Society
Date: 12-2015
Publisher: American Meteorological Society
Date: 2021
Abstract: Estimates of observed long-term changes in daily precipitation globally have been limited due to availability of high-quality observations. In this study, a new gridded dataset of daily precipitation, called Rainfall Estimates on a Gridded Network (REGEN) V1–2019, was used to perform an assessment of the climatic changes in precipitation at each global land location (except Antarctica). This study investigates changes in the number of wet days (≥1 mm) and the entire distribution of daily wet- and all-day records, in addition to trends in annual and seasonal totals from daily records, between 1950 and 2016. The main finding of this study is that precipitation has intensified across a majority of land areas globally throughout the wet-day distribution. This means that when it rains, light, moderate, or heavy wet-day precipitation has become more intense across most of the globe. Widespread increases in the frequency of wet days are observed across Asia and the United States, and widespread increases in the precipitation intensity are observed across Europe and Australia. Based on a comparison of spatial pattern of changes in frequency, intensity, and the distribution of daily totals, we propose that changes in light and moderate precipitation are characterized by changes in precipitation frequency, whereas changes in extreme precipitation are primarily characterized by intensity changes. Based on the uncertainty estimates from REGEN, this study highlights all results in the context of grids with high-quality observations.
Publisher: Wiley
Date: 11-09-2013
DOI: 10.1002/JOC.3588
Publisher: Springer Science and Business Media LLC
Date: 18-09-2013
Publisher: American Geophysical Union (AGU)
Date: 10-2016
DOI: 10.1002/2016JD025480
Abstract: Knowledge about long‐term changes in climate extremes is vital to better understand multidecadal climate variability and long‐term changes and to place today's extreme events in a historical context. While global changes in temperature and precipitation extremes since the midtwentieth century are well studied, knowledge about century‐scale changes is limited. This paper analyses a range of largely independent observations‐based data sets covering 1901–2010 for long‐term changes and interannual variability in daily scale temperature and precipitation extremes. We compare across data sets for consistency to ascertain our confidence in century‐scale changes in extremes. We find consistent warming trends in temperature extremes globally and in most land areas over the past century. For precipitation extremes we find global tendencies toward more intense rainfall throughout much of the twentieth century however, local changes are spatially more variable. While global time series of the different data sets agree well after about 1950, they often show different changes during the first half of the twentieth century. In regions with good observational coverage, gridded observations and reanalyses agree well throughout the entire past century. Simulations with an atmospheric model suggest that ocean temperatures and sea ice may explain up to about 50% of interannual variability in the global average of temperature extremes, and about 15% in the global average of moderate precipitation extremes, but local correlations are mostly significant only in low latitudes.
Publisher: American Geophysical Union (AGU)
Date: 19-01-2016
DOI: 10.1002/2015JD024053
Publisher: IOP Publishing
Date: 27-11-2019
Abstract: The Tibetan Plateau (TP) is the largest and highest upland on Earth. Warming on the TP is faster than that in surrounding areas. Evaluating our understanding of the causes behind these changes provides a test of tools used for projections of future climate in the region. In this study, we analyse the observed changes in twelve extreme temperature indices and compare them with model simulations based on the Coupled Model Intercomparison Project Phase 5 (CMIP5). An optimal fingerprinting method is used to perform detection and attribution analyses on the changes in absolute intensity, percentile-based frequency, fixed threshold exceedances of temperature extremes and diurnal temperature ranges in the central and eastern TP. The results show that during 1958–2017 the TP has experienced increasing intensity and frequency of warm extremes and decreasing intensity and frequency of cold extremes, with almost all these changes larger than those in China and East China. The detection results and attribution analyses show that the anthropogenic (ANT) signal can be robustly detected in the trends for all extreme indices on the TP, and the natural (NAT) signal in some cases, too. The attributable contribution from ANT is estimated to be much larger than that from NAT for most indices. The study also indicates that the CMIP5 models may underestimate the magnitude of warming in some temperature extremes, especially the indices related to cold extremes. This should be kept in mind when informing adaptation decisions on the TP with projections based on the same models.
Publisher: American Geophysical Union (AGU)
Date: 09-01-2016
DOI: 10.1002/2015GL066615
Publisher: Springer Science and Business Media LLC
Date: 07-08-2020
Publisher: American Meteorological Society
Date: 2017
Publisher: Inter-Research Science Center
Date: 09-12-2010
DOI: 10.3354/CR00891
Publisher: The Oceanography Society
Date: 06-2018
Publisher: American Meteorological Society
Date: 09-2013
Publisher: Springer Science and Business Media LLC
Date: 20-01-2016
DOI: 10.1038/NATURE16542
Abstract: Global temperature targets, such as the widely accepted limit of an increase above pre-industrial temperatures of two degrees Celsius, may fail to communicate the urgency of reducing carbon dioxide (CO2) emissions. The translation of CO2 emissions into regional- and impact-related climate targets could be more powerful because such targets are more directly aligned with in idual national interests. We illustrate this approach using regional changes in extreme temperatures and precipitation. These scale robustly with global temperature across scenarios, and thus with cumulative CO2 emissions. This is particularly relevant for changes in regional extreme temperatures on land, which are much greater than changes in the associated global mean.
Publisher: Copernicus GmbH
Date: 06-07-2023
DOI: 10.5194/EMS2023-333
Abstract: A derecho is a widespread, long-lived, straight-line windstorm that is associated with a fast-moving group of severe thunderstorms known as a mesoscale convective system. During 18 August 2022, a highly intense and organized convective storm, classified as a derecho, developed over the western Mediterranean Sea affecting Corsica, northern Italy and Austria, with wind gusts up to 62 m/s and giant hail (~11 cm). There were 12 fatalities and 106 people injured. This event received much attention in the media for its extraordinary impact and the rareness over the Mediterranean Sea. The derecho developed over an extreme marine heatwave that persisted during the whole summer. Therefore, the hypothesis of a relationship between the extreme atmospheric event and the extreme marine heatwave rapidly arose, and thus, a possible link with anthropogenic climate change. This convective event can be considered as extreme from the affected locations point of view (in terms of winds) but also is between one of the most powerful derechos ever recorded in the USA and Europe. Also, the event developed over an extreme marine heatwave that was mainly affecting the western Mediterranean Sea during summer 2022. Here, by performing model simulations with both the NCAR Model for Prediction Across Scales and the M& #233 t& #233 o-France nonhydrostatic operational AROME model, we find a relationship between the marine heatwave, the actual anthropogenic climate change conditions, and the development of this extremely rare and severe convective event. We also find a future worrying increase in intensity, size and duration of such an event with future climate change conditions.
Publisher: IOP Publishing
Date: 09-2023
Abstract: Hot, cold and dry meteorological extremes are often linked with severe impacts on the public health, agricultural, energy and environmental sectors. Skillful predictions of such extremes could therefore enable stakeholders to better plan and adapt to future impacts of these events. The intensity, duration and frequency of such extremes are affected by anthropogenic climate change and modulated by different modes of climate variability. Here we use a large multi-model ensemble from the Coupled Model Intercomparison Project Phase 6 and constrain these simulations by sub-selecting those members whose global SST anomaly patterns are most similar to observations at a given point in time, thereby phasing in the decadal climate variability with observations. Hot and cold extremes are skillfully predicted over most of the globe, with also a widespread added value from using the constrained ensemble compared to the unconstrained full CMIP6 ensemble. On the other hand, dry extremes show skill only in some regions with results sensitive to the index used. Still, we find skillful predictions and added skill for dry extremes in some regions such as western north America, southern central and eastern Europe, southeastern Australia, southern Africa and the Arabian peninsula. We also find that the added skill in the constrained ensemble is due to a combination of improved multi-decadal variations in phase with observed climate extremes and improved representation of long-term changes. Our results demonstrate that constraining decadal variability in climate projections can provide improved estimates of temperature extremes and drought in the next twenty years, which can inform targeted adaptation strategies to near-term climate change.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-16449
Abstract: Seasonal Forecasts are critical tools for early-warning decision support systems, that can help reduce the related risk associated with hot or cold weather and other events that can strongly affect a multitude of socio-economic sectors. Recent advances in both statistical approaches and numerical modeling have improved the skill of Seasonal Forecasts. However, especially in mid-latitudes, they are still affected by large uncertainties that can limit their usefulness. The MSCA-H2020 project ARTIST aims at improving our knowledge of climate predictability at the seasonal time-scale, focusing on the role of unexplored drivers, to finally enhance the performance of current prediction systems. This effort is meant to reduce uncertainties and make forecasts efficiently usable by regional meteorological services and private bodies. This study focuses on seasonal prediction of heat extremes in Europe, and here we present a first attempt to predict heat wave accumulated activity across different target seasons. An empirical seasonal forecast is designed based on Machine Learning techniques. A feature selection approach is used to detect the best subset of predictors among a variety of candidates, and then an assessment of the relative importance of each predictor is done, in different European regions for the four main seasons.Results show that many observed teleconnections are caught by the data-driven approach, while a few features that show to be linked to the heat wave propensity of a season deserve a deeper understanding of the underpinning physical process.
Publisher: American Geophysical Union (AGU)
Date: 05-09-2018
DOI: 10.1029/2018GL079102
Publisher: Springer Science and Business Media LLC
Date: 25-06-2018
Publisher: Wiley
Date: 31-12-2013
DOI: 10.1002/JOC.3899
Publisher: Ubiquity Press, Ltd.
Date: 2021
DOI: 10.5334/DSJ-2021-007
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-15594
Abstract: Stakeholders in all socio-economic sectors require reliable forecasts at multiple timescales as part of their decision-making processes. Although basing decisions mostly on a particular timescale (e.g., weather, subseasonal, seasonal) is the present status quo, this approach tends to lead to missing opportunities for more comprehensive risk-management systems (Goddard et al. 2014).& While today a variety of forecasts are produced targeting distinct timescales in a routine way, these products are generally presented to the users in different websites and bulletins, often without an assessment of how consistent the predictions are across timescales. Since different models and strategies are used at different timescales by both national and international seasonal and subseasonal forecasting centers (Kirtman et al. 2014, Kirtman et al. 2017, Vitart et al. 2017), and skill is different at those timescales, it is key to guarantee that a physically consistent & #8220 bridging& #8221 between the forecasts exists, and that the cross-timescale predictions are overall skilful and actionable, so decision makers can conduct their work.& Here, we propose and explore a new methodology & #8211 that we call the Xit (& #8220 cross-it& #8221 ) operator& #8211 based on the Liang-Kleeman information flow (e.g., Tawia Hagan et al. 2019) and wavelet spectra and entropy (e.g., Zunino et al. 2007), to & #8220 bridge& #8221 forecasts at different timescales in a smooth and physically-consistent manner.& In summary, the Xit& operator (1) conducts a wavelet spectral analysis (e.g., Ng and Chan 2013, Zunino et al. 2007) and (2) a non-stationary time-frequency causality analysis (e.g., Tawia Hagan et al. 2019, Liang 2015) on forecasts at different timescales to assess cross-timescale coherence and physical consistency in terms of various sources of predictability. In principle, the approach permits to identify which & #8220 intrinsic& #8221 periods/scales (i) in the timescale continuum (t) are more suitable for the bridging to occur, and/or which ones can produce more skillful forecasts, by pointing to particular target times& #8212 i.e., potential windows of opportunity (Mariotti et al. 2020)& #8212 in the forecast period where wavelet entropy (uncertainty) is lower.& While the first component of the Xit& operator, i.e., the wavelet spectral and entropy analysis (Zunino et al. 2007), is designed to identify the optimal time-frequency bands for cross-timescale bridging, the fact that two forecast systems (e.g., a subseasonal and a seasonal) exhibit significant wavelet coherence does not imply that bridging those systems will provide physically-consistent predictions. The second component of the Xit operator, i.e., the non-stationary causality analysis (Tawia Hagan et al. 2019), is thus designed to assess physical consistency of the bridging by analyzing the causal link between different climate drivers (acting at different timescales) and the forecast variable of interest.
Publisher: American Geophysical Union (AGU)
Date: 10-07-2023
DOI: 10.1029/2022GL102493
Abstract: The impacts of hot, dry, and compound hot‐dry extremes are significant for societies, economies, and ecosystems worldwide. Such events therefore need to be assessed in the light of anthropogenic climate change so that suitable adaptation measures can be implemented by governments and stakeholders. Here we show a comprehensive analysis of hot, dry, and compound hot‐dry extremes over global land regions using 25 Coupled Model Intercomparison Project Phase 6 models and four future emissions scenarios from 1950 to 2100. Hot, dry, and compound hot‐dry extremes are projected to increase over large parts of the globe by the end of the 21st century. Hot and compound hot‐dry extremes show the most widespread increases and dry extreme changes are sensitive to the index used. Many regional changes depend on the strength of greenhouse‐gas forcing, which highlights the potential to limit the changes with strong mitigation efforts.
Publisher: American Meteorological Society
Date: 08-2016
DOI: 10.1175/2016BAMSSTATEOFTHECLIMATE.1
Abstract: Editor’s note: For easy download the posted pdf of the State of the Climate for 2016 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.
Publisher: American Geophysical Union (AGU)
Date: 22-06-2020
DOI: 10.1029/2019GL086875
Abstract: This study conducts a detection and attribution analysis of the observed changes in extreme precipitation during 1951–2015. Observed and CMIP6 multimodel simulated changes in annual maximum daily and consecutive 5‐day precipitation are compared using an optimal fingerprinting technique for different spatial scales from global land, Northern Hemisphere extratropics, tropics, three continental regions (North America and western and eastern Eurasia), and global “dry” and “wet” land areas (as defined by their average extreme precipitation intensities). Results indicate that anthropogenic greenhouse gas influence is robustly detected in the observed intensification of extreme precipitation over the global land and most of the subregions considered, all with clear separation from natural and anthropogenic aerosol forcings. Also, the human‐induced greenhouse gas increases are found to be a dominant contributor to the observed increase in extreme precipitation intensity, which largely follows the increased moisture availability under global warming.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-2399
Abstract: Both global warming and internal climate variability modulate changes in the intensity and frequency of extreme climate events. Anticipating such variations years in advance may help minimise the impact on climate-vulnerable sectors and society, as well as enable short-term adaptation strategies and early-warning systems in a changing climate. Decadal climate predictions are a source of climate information for multi-annual timescales. They are provided by climate forecast systems similar to the models used for long-term climate projections but that have been initialised with the best estimate of the contemporaneous conditions of the climate system. However, before using the predictions, the forecast quality should be assessed. This is an essential step to evaluate the accuracy of the predictions and find windows of opportunity (variables/indices, regions and forecast times) to provide climate services with data of sufficient quality to satisfy the user requirements.We evaluate the deterministic and probabilistic forecast quality of the multi-model ensemble built with all the available decadal hindcasts (i.e., retrospective decadal predictions) contributing to CMIP6, which consists of a total of 133 ensemble members from 13 forecast systems. The forecast quality assessment has been performed for predictions of seasonal and annual indices of daily temperature and precipitation extremes for the forecast years 1-5. These indices measure the intensity and frequency of hot and cold temperature extremes, and the intensity and rainfall accumulation related to heavy precipitation extremes. The prediction skill for the temperature and precipitation extreme indices is further compared to the skill for mean temperature and precipitation, respectively. In order to assess the impact of the model initialisation, the predictions are compared against historical forcing simulations (i.e., retrospective climate projections) created with the same models, consisting of a total of 134 ensemble members from the same forecast systems as the decadal hindcasts.We find that the decadal hindcasts skillfully predict both mean and extreme temperature indices over most of the globe for multi-annual periods. The forecast quality for mean precipitation and extreme precipitation indices is generally low, and significant skill is found only over some limited regions. The reduced quality of the precipitation predictions with respect to temperature is due to the relatively smaller effect of human-induced warming for this variable. The comparison between the skill for mean variables and extreme indices shows that the extreme indices are generally predicted with lower skill, especially those related to the intensity of extreme events. We find generally small and region-dependent improvements from model initialisation compared to historical forcing simulations. The added value due to initialisation is higher for the mean variables than for the extreme indices. Besides, such skill differences differ between indices, especially those representing extreme temperature. This systematic evaluation of decadal hindcasts is essential when providing a climate service based on decadal predictions so that the user is informed about the trustworthiness of the forecasts for each specific region and extreme event. Also, comparing decadal hindcast and historical simulations may help climate services providers select the highest-quality information from these different data sources.
Publisher: Wiley
Date: 10-10-2015
DOI: 10.1002/JOC.4174
Publisher: American Geophysical Union (AGU)
Date: 17-05-2016
DOI: 10.1002/2015JD024583
Publisher: Annual Reviews
Date: 03-01-2021
DOI: 10.1146/ANNUREV-MARINE-032720-095144
Abstract: Ocean temperature variability is a fundamental component of the Earth's climate system, and extremes in this variability affect the health of marine ecosystems around the world. The study of marine heatwaves has emerged as a rapidly growing field of research, given notable extreme warm-water events that have occurred against a background trend of global ocean warming. This review summarizes the latest physical and statistical understanding of marine heatwaves based on how they are identified, defined, characterized, and monitored through remotely sensed and in situ data sets. We describe the physical mechanisms that cause marine heatwaves, along with their global distribution, variability, and trends. Finally, we discuss current issues in this developing research area, including considerations related to thechoice of climatological baseline periods in defining extremes and how to communicate findings in the context of societal needs.
Publisher: American Geophysical Union (AGU)
Date: 17-05-2016
DOI: 10.1002/2015JD024584
Publisher: American Geophysical Union (AGU)
Date: 19-08-2020
DOI: 10.1029/2019JD032263
Abstract: We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org .
Publisher: Copernicus GmbH
Date: 11-04-2022
DOI: 10.5194/EGUSPHERE-2022-98
Abstract: Abstract. Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure and precipitation on decadal to multi-decadal time scales. We find that the constrained projections show significant skill in predicting the climate of the following ten to twenty years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first two decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the ex le of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades.
Publisher: Copernicus GmbH
Date: 25-10-2011
DOI: 10.5194/NHESS-11-2821-2011
Abstract: Abstract. A refined model for the calculation of storm losses is presented, making use of high-resolution insurance loss records for Germany and allowing loss estimates on a spatial level of administrative districts and for single storm events. Storm losses are calculated on the basis of wind speeds from both ERA-Interim and NCEP reanalyses. The loss model reproduces the spatial distribution of observed losses well by taking specific regional loss characteristics into account. This also permits high-accuracy estimates of total cumulated losses, though slightly underestimating the country-wide loss sums for storm "Kyrill", the most severe event in the insurance loss records from 1997 to 2007. A larger deviation, which is assigned to the relatively coarse resolution of the NCEP reanalysis, is only found for one specific rather small-scale event, not adequately captured by this dataset. The loss model is subsequently applied to the complete reanalysis period to extend the storm event catalogue to cover years when no systematic insurance records are available. This allows the consideration of loss-intensive storm events back to 1948, enlarging the event catalogue to cover the recent 60+ years, and to investigate the statistical characteristics of severe storm loss events in Germany based on a larger s le than provided by the insurance records only. Extreme value analysis is applied to the loss data to estimate the return periods of loss-intensive storms, yielding a return period for storm "Kyrill", for ex le, of approximately 15 to 21 years.
Publisher: IEEE
Date: 19-11-2020
Publisher: Growing Science
Date: 2019
Publisher: IOP Publishing
Date: 03-2017
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-11143
Abstract: The forecast quality of multi-model seasonal-to-decadal climate predictions, as measured by metrics of, among others, accuracy and reliability, has been traditionally estimated considering time-average products and products for event thresholds that do not target the occurrence of unusual events of either monthly or seasonal duration. However, there is an increasing interest in some user communities for products that represent extreme and unusual events. This presentation will discuss the differences in forecast quality between traditional forecast products, like mean seasonal temperature, and products for intraseasonal extremes (e.g., those measured with the 95th percentile of high-frequency temperature over periods like a month or a season) and monthly and seasonal unusual events (such as the frequency of exceeding the 90th percentile of the daily climatological distribution of temperature at a given time of the year). The results will be discussed in the context of their implications to address a number of user requirements from different sectors. The relevance of the forecast quality estimated from hindcast sets and the role of the observational uncertainty will be discussed when delivering forecast products in a climate service context. The implications of this work for the standardisation of climate services based on climate predictions will also be discussed.
Publisher: American Geophysical Union (AGU)
Date: 27-10-2018
DOI: 10.1029/2017JD027958
Publisher: Cambridge University Press
Date: 14-10-2013
Publisher: American Geophysical Union (AGU)
Date: 04-03-2013
DOI: 10.1002/JGRD.50150
Publisher: Wiley
Date: 23-07-2009
DOI: 10.1002/JOC.1982
Publisher: American Meteorological Society
Date: 10-2019
Start Date: 2015
End Date: 2017
Funder: Australian Research Council
View Funded ActivityStart Date: 2015
End Date: 02-2018
Amount: $367,536.00
Funder: Australian Research Council
View Funded Activity