ORCID Profile
0000-0001-5504-6450
Current Organisations
University of Oxford
,
University of Bristol
,
Cardiff University
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Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-3533
Abstract: & & In 2018 the long rains season in Kenya (March-May) was the wettest ever recorded. The country experienced several multi-day heavy rainfall episodes, leading to dam collapse, land and mudslides. 186 people died due to flooding and 300,000 were left displaced.& & & & & The Kenya Meteorological Department issued several advisories during the season that warned of heavy rainfall events a few days before their occurrence. Ahead of this no warnings were given.& & & & However subseasonal forecasts gave strong indications of the heaviest rainfall episodes, several weeks in advance. With this extra lead time, preparedness actions may have been taken in order to reduce flood risk and save lives.& & & & & To this end, the ForPAc project (Toward Forecast-Based Preparedness Action) has been working in partnerships across Kenya and the UK to evaluate and build trust in subseasonal forecasts, and explore preparedness actions which could be taken in response. Most recently ForPAc has been granted access to real-time subseasonal data as part of phase two of the S2S pilot.& & & & In this presentation we will first show analysis of the S2S hindcasts over East Africa, demonstrating the relatively high levels of subseasonal forecast skill and linking this to a strong MJO teleconnection that models capture relatively well.& & & & In the second part we will describe work with stakeholders to co-design forecast products derived from the S2S data, concluding with a report on the forecasts for the ongoing 2020 long rains season and an evaluation of the way in which these have influenced disaster preparedness.& &
Publisher: Wiley
Date: 06-04-2017
DOI: 10.1002/MET.1654
Publisher: Copernicus GmbH
Date: 05-04-2016
Publisher: Authorea, Inc.
Date: 27-12-2022
DOI: 10.22541/ESSOAR.167214223.33785715/V1
Abstract: The Horn of Africa drylands (HAD) are among the most vulnerable regions to hydroclimatic extremes. The two rainfall seasons — long and short rains — exhibit high intraseasonal and interannual variability. Accurately simulating the long and short rains has proven to be a significant challenge for the current generation of weather forecast and climate models, revealing key gaps in our understanding of the drivers of rainfall in the region. In contrast to existing climate modelling and observation-based studies, here we analyze the HAD rainfall from an observationally-constrained Lagrangian perspective. We quantify and map the major oceanic and terrestrial sources of moisture driving the variability in the long and short rains. Specifically, our results show that the Arabian Sea (through its influence on the northeast monsoon circulation) and the southern Indian Ocean (via the Somali low level jet) contribute ~80% of the HAD rainfall. We see that moisture contributions from land sources are very low at the beginning of each season, but supply up to ~20% from the second month onwards, i.e., when the oceanic-origin rainfall has already increased water availability over land. Further, our findings suggest that the interannual variability in the long and short rains is driven by changes in circulation patterns and regional thermodynamic processes rather than changes in ocean evaporation. Our results can be used to better evaluate, and potentially improve, numerical weather prediction and climate models, which has important implications for (sub-)seasonal forecasts and long-term projections of the HAD rainfall.
Publisher: Wiley
Date: 28-10-2021
DOI: 10.1002/ASL.1015
Publisher: Copernicus GmbH
Date: 25-01-2023
Abstract: Abstract. Potential evapotranspiration (PET) represents the evaporative demand in the atmosphere for the removal of water from the land and is an essential variable for understanding and modelling land–atmosphere interactions. Weather generators are often used to generate stochastic rainfall time series however, no such model exists for the generation of a stochastically plausible PET time series. Here we develop a stochastic PET generator, stoPET, by leveraging a recently published global dataset of hourly PET at 0.1∘ resolution (hPET). stoPET is designed to simulate realistic time series of PET that capture the diurnal and seasonal variability in hPET and to support the simulation of various scenarios of climate change. The parsimonious model is based on a sine function fitted to the monthly average diurnal cycle of hPET, producing parameters that are then used to generate any number of synthetic series of randomised hourly PET for a specific climate scenario at any point of the global land surface between 55∘ N and 55∘ S. In addition to supporting a stochastic analysis of historical PET, stoPET also incorporates three methods to account for potential future changes in atmospheric evaporative demand to rising global temperature. These include (1) a user-defined percentage increase in annual PET, (2) a step change in PET based on a unit increase in temperature, and (3) the extrapolation of the historical trend in hPET into the future. We evaluated stoPET at a regional scale and at 12 locations spanning arid and humid climatic regions around the globe. stoPET generates PET distributions that are statistically similar to hPET and an independent PET dataset from CRU, thereby capturing their diurnal/seasonal dynamics, indicating that stoPET produces physically plausible diurnal and seasonal PET variability. We provide ex les of how stoPET can generate large ensembles of PET for future climate scenario analysis in sectors like agriculture and water resources with minimal computational demand.
Publisher: Copernicus GmbH
Date: 15-09-2020
DOI: 10.5194/HESS-2016-28
Abstract: Abstract. Soil moisture memory is a key component of seasonal predictability. However uncertainty in current memory estimates is not clear and it is not obvious to what extent these are dependent on model uncertainties. To address this question, we perform a global sensitivity analysis of memory to key hydraulic parameters, using an uncoupled version of the land surface model H-TESSEL. Results show significant dependency of estimates of memory and its uncertainty on these parameters, suggesting that operational seasonal forecasting models using deterministic hydraulic parameter values are likely to display a narrower range of memory than exists in reality. Explicitly incorporating hydraulic parameter uncertainty in models may then give improvements in forecast skill and reliability, as has been shown elsewhere in the literature. Our results also show significant differences with with previous estimates of memory uncertainty, warning against placing too much confidence in a single quantification of uncertainty.
Publisher: Wiley
Date: 25-02-2022
DOI: 10.1002/WEA.4161
Publisher: Copernicus GmbH
Date: 17-02-2016
Publisher: Copernicus GmbH
Date: 19-01-2021
Publisher: Springer Science and Business Media LLC
Date: 24-08-2021
DOI: 10.1038/S41597-021-01003-9
Abstract: Challenges exist for assessing the impacts of climate and climate change on the hydrological cycle on local and regional scales, and in turn on water resources, food, energy, and natural hazards. Potential evapotranspiration (PET) represents atmospheric demand for water, which is required at high spatial and temporal resolutions to compute actual evapotranspiration and thus close the water balance near the land surface for many such applications, but there are currently no available high-resolution datasets of PET. Here we develop an hourly PET dataset (hPET) for the global land surface at 0.1° spatial resolution, based on output from the recently developed ERA5-Land reanalysis dataset, over the period 1981 to present. We show how hPET compares to other available global PET datasets, over common spatiotemporal resolutions and time frames, with respect to spatial patterns of climatology and seasonal variations for selected humid and arid locations across the globe. We provide the data for users to employ for multiple applications to explore diurnal and seasonal variations in evaporative demand for water.
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-5330
Abstract: & & The Horn of Africa drylands (HAD) are highly vulnerable to hydroclimatic extremes, with droughts and floods frequently leading to famines, crop losses, and significant humanitarian crises. However, development of robust mitigation measures has been hindered by the lack of understanding of the drivers of the two main rainfall seasons in the region: the long (March& #8211 May) and short (October& #8211 December) rains. In particular, the inter-annual variability of the long rains has been subject of much debate a significant amount of research has attempted to diagnose the drivers of the observed decline in the long rains. Given the ecological and socio-economic importance of the two rain seasons for the HAD region, understanding the major moisture sources and their variability in both space and time is essential. Such an analysis can help disentangle the causes of temporal variability in rainfall, especially the long rains, improve forecasts, and build ecosystem and community resilience against hydroclimatic extremes.& & & & To trace the origin of rainfall over the HAD region, we use global simulations of the FLEXPART version 9.01, forced with the ERA-Interim reanalysis for a period of 37 years (1980& #8211 ). The FLEXPART outputs include the properties of the air parcels at 3-hourly time steps, which are then post-processed to identify the source regions of rainfall using the Heat and Moisture Tracking Framework (HAMSTER v1.2.0) described by Keune et al. (2021). Using this framework, we first trace the rainfall occurring over the HAD region during the long and short rain seasons to their terrestrial and oceanic sources spatially. Then, we track the changes in the contributions of ocean and land evaporation to HAD rainfall in time over the 37-year period.& & & & & Preliminary results show that around 80% of HAD rainfall originates from Indian Ocean evaporation, for both seasons. For both seasons the contribution of evaporation from land is relatively low compared to the oceanic contribution. For the long rains, a similar amount of moisture originates from recycling (local) and remote sources (10.9% and 10.5% respectively). On the other hand the short rains show a larger proportion of local recycling (13.8%) relative to remote land evaporation (9.4%). The larger contribution of remote land sources for the long rains arises from the Indian subcontinent and Southeast Asia. Further, we shed light on the trends and anomalies in source regions for the two rain seasons, with particular focus on the anomalies in moisture sources that are characteristic of extreme dry and wet conditions.& & & & & strong& References:& /strong& & & & & Keune, J., Schumacher, D. L., and Miralles, D. G.: A holistic framework to estimate the origins of atmospheric moisture and heat using a Lagrangian model, Geosci. Model Dev. Discuss. [preprint], in review, 2021.& &
Publisher: Copernicus GmbH
Date: 27-04-2020
Abstract: Abstract. Preparedness saves lives. Forecasts can help improve preparedness by triggering early actions as part of a pre-defined protocols under the Forecast-based Action/Finance (FbA) approach, however it is essential to understand the skill of a forecast before using it as a trigger. In order to support the development of early action protocols over Kenya we evaluate the 33 heavy rainfall advisories (HRA) issued by the Kenya Meteorological Department (KMD) during 2015–2019. The majority of HRA warn counties which go on to receive heavy rainfall. However in general the total area warned is much larger than the extent of significant rainfall. The three periods of flood impacts during 2018 and 2019 were all preceded by HRA, which warned the counties with recorded losses. By contrast, none of the four flooding periods in 2015–2017 were preceded by HRA. We suggest that access to the UK Met Office Global Hazard Map (GHM) at KMD at the end of 2017 was a key factor in this step-change in skill. Overall we find that KMD HRA effectivly warn of heavy rainfall and flooding and can be a vital source of information for early preparedness. However a lack of spatial detail on flood impacts limits their utility for systematic FbA approaches. We conclude with suggestions for making the HRA more useful for FbA, and outline the developing approach to flood forecasting in Kenya.
Publisher: American Meteorological Society
Date: 31-10-2019
Abstract: El Niño–Southern Oscillation (ENSO) has large socioeconomic impacts worldwide. The positive phase of ENSO, El Niño, has been linked to intense rainfall over East Africa during the short rains season (October–December). However, we show here that during the extremely strong 2015 El Niño the precipitation anomaly over most of East Africa during the short rains season was less intense than experienced during previous El Niños, linked to less intense easterlies over the Indian Ocean. This moderate impact was not indicated by reforecasts from the ECMWF operational seasonal forecasting system, SEAS5, which instead forecast large probabilities of an extreme wet signal, with stronger easterly anomalies over the surface of the Indian Ocean and a colder eastern Indian Ocean/western Pacific than was observed. To confirm the relationship of the eastern Indian Ocean to East African rainfall in the forecast for 2015, atmospheric relaxation experiments are carried out that constrain the east Indian Ocean lower troposphere to reanalysis. By doing so the strong wet forecast signal is reduced. These results raise the possibility that link between ENSO and Indian Ocean dipole events is too strong in the ECMWF dynamical seasonal forecast system and that model predictions for the East African short rains rainfall during strong El Niño events may have a bias toward high probabilities of wet conditions.
Publisher: Informa UK Limited
Date: 26-11-2021
Publisher: Wiley
Date: 02-04-2018
DOI: 10.1002/ASL.815
Publisher: Wiley
Date: 07-2017
DOI: 10.1002/QJ.3094
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-10577
Abstract: & & Land use and land cover change significantly influence regional energy budgets, and hydrological and biogeochemical cycles which may occur from both anthropogenic and natural disturbances. Likewise, vegetation may also respond dynamically to climate. In the past decades, the Horn of Africa has been hit by several droughts and heatwaves causing severe economic, environmental, and social damage. To evaluate and mitigate such impacts, it is necessary to establish and quantify the linkage between land cover change and regional climate. This study presents an observational analysis of recent (2001& #8211 ) historical changes in land cover and land use and their relation to climate in the Horn of Africa.& & & & & Firstly, we evaluate changes in land cover using the Moderate Resolution Imaging Spectrometer (MODIS) dataset. Results indicate steady expansion of grasslands (net gain is 1.2% of total area) and an opposite pattern for open shrublands during the period 2001& #8211 . Importantly, deforestation of evergreen broadleaf forest (0.3% of the total area) is also noticeable in continuous fractional vegetation cover (FVC) analysis. Next, the Global Database of Historical Yields (GDHY) is explored to identify the yield trends for two main cereals: maize and wheat.& Wheat yield shows increasing trends in the northern and southern parts, while maize yields increase in Ethiopia and mildly decrease in Kenya. To quantify the adverse impact of drought on yields, three drought indices are used: (a) Standardized Precipitation Evapotranspiration Index (SPEI), (b) self-calibrating Palmer Drought Severity Index (scPDSI), and (c) Standard Evapotranspiration Deficit Index (SEDI). The analysis identifies SPEI12 as arguably the best performing drought index for monitoring and forecasting impacts on yields in this region.& & & & & Finally, a Conditional Spectral Granger Causality (CSGC) algorithm is employed for understanding the influence of climate variability on vegetation dynamics. Although the influence of climatic factors (i.e., precipitation, temperature, and solar energy radiation) on vegetation dynamics is heterogeneous, given the wide spectrum of climate regimes in the region, an overall increased influence of temperature on vegetation dynamics is revealed. In conclusion, the observational evidence indicates that climate plays an important role as a driver of both crop and natural vegetation change in the Horn of Africa.& & &
Publisher: Springer Science and Business Media LLC
Date: 11-06-2019
Publisher: Copernicus GmbH
Date: 11-05-2022
DOI: 10.5194/GMD-2022-128
Abstract: Abstract. Potential evapotranspiration (PET) represents the evaporative demand in the atmosphere for the removal of water from the land and is an essential variable for understanding and modelling land-atmosphere interactions. Weather generators are often used to generate stochastic rainfall time series however, no such model exists for stochastically generating plausible PET time series. Here we develop a stochastic PET generator, stoPET, by leveraging a recently published global dataset of hourly PET at 0.1° resolution (hPET). stoPET is designed to simulate realistic time series of PET that capture the diurnal and seasonal variability of hPET and to support the simulation of various scenarios of climate change. The parsimonious model is based on a sine function fitted to the monthly average diurnal cycle of hPET, producing parameters that are then used to generate synthetic series of hourly PET at any 0.1° land surface point between 55° N and 55° S. stoPET also incorporates three methods to account for potential future changes in atmospheric evaporative demand to rising global temperature. These include 1) user-defined percentage increase of annual PET 2) a step change in PET based on a unit increase in temperature, and 3) extrapolation of the historical trend in hPET into the future. We evaluated stoPET at a regional scale and at twelve locations spanning arid and humid climatic regions around the globe. stoPET generates PET distributions that are statistically similar to hPET, capturing its diurnal/seasonal dynamics, indicating that stoPET produces physically plausible diurnal and seasonal PET variability. We provide ex les of how stoPET can generate large ensembles of PET for future climate scenario analysis in sectors like agriculture and water resources, with minimal computational demand.
Publisher: American Geophysical Union (AGU)
Date: 04-11-2022
DOI: 10.1029/2022GL099299
Abstract: Rural communities in the Horn of Africa Drylands (HADs) are increasingly vulnerable to multi‐season droughts due to the strong dependence of livelihoods on seasonal rainfall. We analyzed multiple observational rainfall data sets for recent decadal trends in mean and extreme seasonal rainfall, as well as satellite‐derived terrestrial water storage and soil moisture trends arising from two key rainfall seasons across various subregions of HAD. We show that, despite decreases in total March‐April‐May rainfall, total water storage in the HAD has increased. This trend correlates strongly with seasonal totals and especially with extreme rainfall in the two dominant HAD rainy seasons between 2003 and 2016. We further show that high‐intensity October‐November‐December rainfall associated with positive Indian Ocean Dipole events lead to the largest seasonal increases in water storage that persist over multiple years. These findings suggest that developing groundwater resources in HAD could offset or mitigate the impacts of increasingly common droughts.
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-2129
Abstract: & & Skillful weather and climate forecasts, if utilized effectively, have the potential to improve preparedness and disaster risk reduction. Forecast-based Action (FbA) is a framework for aiding decisions on preparedness in advance of weather/climate hazards, through use of forecasts. Here, we present a summary of research results and pilot project work within the Arid and Semi-Arid Land (ASAL) areas of Kenya conducted under the Towards Forecast-based Preparedness and Action (ForPAc) project. We also present opportunities for scaling up FbA & across the Greater Horn of Africa region through leveraging on connected projects and initiatives like Down2Earth. & Skill assessment of a pool of weather/climate models has established the most skilful multi-model combinations for monthly-seasonal timescale. & Co-production initiatives between forecast users and producers established the forecast variables best aligned with Kenya& #8217 s existing Drought Early Warning Systems (DEWS) Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI) and soil moisture, as well as optimum forecast delivery time required by the DEWS processes. Our analysis shows that rainfall forecasts have skill across & #8216 seamless& #8217 sub-seasonal to seasonal lead times, offering the potential to improve the anticipatory actions within the DEWS of Kitui county of Kenya. Working with multiple stake-holders from across local and national government, humanitarian agencies, forecasting services and climate researchers, we have explored the potential for a more anticipatory, proactive DEWS using forecast information. The Down2Earth project, which aims at translating climate information for adaptation and climate-resilience across decision-making levels is leveraging on gains of ForPAc by advancing FbA approaches within the rural communities of Kenya, Somalia and Ethiopia. To facilitate the institutionalization of FbA, we have developed a regional roadmap to guide implementation within National, regional and international humanitarian actors. & & & &
Publisher: Public Library of Science (PLoS)
Date: 17-03-2023
DOI: 10.1371/JOURNAL.PCLM.0000138
Abstract: Seasonal rainfall forecasts support early preparedness. These forecasts are typically disseminated at Regional Climate Outlook Forums (RCOFs), in the form of seasonal tercile probability categories—above normal, normal, below normal. However, these categories cannot be related directly to impacts on terrestrial water stores within catchments, since they are mediated by non-linear hydrological processes occurring on fine spatiotemporal scales, including rainfall partitioning into infiltration, evapotranspiration, runoff and groundwater recharge. Hydrological models are increasingly capable of capturing these processes, but there is no simple way to drive such models with a specific RCOF seasonal tercile rainfall forecast. Here we demonstrate a new method, “Quantile Bin Res ling” (QBR), for producing seasonal water forecasts for a drainage basin by integrating a tercile seasonal rainfall forecast with a hydrological model. QBR is based on mapping historical quantiles of basin-average rainfall to historical simulations of the water balance, and circumvents challenges associated with using climate model output to drive impact models directly. We evaluate QBR by generating 35 years of seasonal reforecasts for various water balance stores and fluxes for the Upper Ewaso Ng’iro basin in Kenya. Hindcasts indicate that when input tercile rainfall forecasts have skill, QBR provides accurate water forecasts at kilometre-scale resolution, which is relevant for anticipatory action down to village level. Pilot operational experimental water forecasts were produced for this basin using QBR for the 2022 March-May rainfall season, then disseminated to regional stakeholders at the Greater Horn of Africa Climate Outlook Forum (GHACOF). We discuss this initiative, along with limitations, plans and future potential of the method. Beyond the demonstrated application to water-related forecasts, QBR can be easily adapted to work with any rainfall-driven impact model. It can translate objective tercile climate probabilities into impact-relevant water balance forecasts at high spatial resolution in an efficient, transparent and flexible way.
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-2804
Abstract: & & Early warning of drought conditions can help protect lives and livelihoods, especially in dry regions of subsistence agriculture and pastoralism. Regions such as the Horn of Africa Drylands (HAD) may benefit from advance warning of changes to available water supplies, as rural communities make critical decisions about planting and moving livestock at particular points in time. However whilst the regionally-mandated seasonal forecast for HAD provides information on rainfall totals, it does not quantify expected impacts on water balance components such as soil moisture and groundwater storage. This latter information may be more useful to rural communities who rely on groundwater for water resources for humans and livestock, and soil moisture for crop growth. These hydrological quantities can typically be estimated with hydrological models, but in drylands the processes governing water partitioning are complex and largely unrepresented in most existing regional and global hydrological models.& & & & & & & & & & Here we leverage the capability of a dryland-specific hydrological model (DRYP) to produce rainfall-driven water security forecasts for HAD. DRYP incorporates spatially varying rainfall and evaporative demand, dynamic surface-groundwater interactions, ephemeral flow through channels and focused groundwater recharge. We employ DRYP in a pilot application to produce seasonal forecasts of soil moisture and groundwater recharge for a large catchment within the HAD. We use the objective seasonal forecasts provided by the IGAD Climate Prediction and Application Centre (ICPAC) and disseminated within the Greater Horn of Africa Climate Outlook Forum (GHACOF). Methodological approaches to integrate DRYP with the regional climate outlook disseminated by ICPAC are described, along with evaluation of potential skill of these new water security forecasts for the regional pilot catchment. Finally, we describe and update on an active forecast pilot activity, where water security forecasts for the current rainfall season (March-May 2022) have been co-produced with ICPAC and disseminated to stakeholders in February 2022 as part of the GHACOF event, now publicly available via the ICPAC East Africa Hazards Watch platform, under the EU H2020-funded DOWN2EARTH project. Co-design activity arising from recent stakeholder workshops will be described.& &
Publisher: American Geophysical Union (AGU)
Date: 23-06-2023
DOI: 10.1029/2022JD038408
Abstract: The Horn of Africa drylands (HAD) are among the most vulnerable regions to hydroclimatic extremes. The two rainfall seasons—long and short rains—exhibit high intraseasonal and interannual variability. Accurately simulating the long and short rains has proven to be a significant challenge for the current generation of weather and climate models, revealing key gaps in our understanding of the drivers of rainfall in the region. In contrast to existing climate modeling and observation‐based studies, here we analyze the HAD rainfall from an observationally‐constrained Lagrangian perspective. We quantify and map the region's major oceanic and terrestrial sources of moisture. Specifically, our results show that the Arabian Sea (through its influence on the northeast monsoon circulation) and the southern Indian Ocean (via the Somali low‐level jet) contribute ∼80% of the HAD rainfall. We see that moisture contributions from land sources are very low at the beginning of each season, but supply up to ∼20% from the second month onwards, that is, when the oceanic‐origin rainfall has already increased water availability over land. Further, our findings suggest that the interannual variability in the long and short rains is driven by changes in circulation patterns and regional thermodynamic processes rather than changes in ocean evaporation. Our results can be used to better evaluate, and potentially improve, numerical weather prediction and climate models, and have important implications for (sub‐)seasonal forecasts and long‐term projections of the HAD rainfall.
Publisher: Wiley
Date: 12-07-2018
DOI: 10.1002/MET.1660
Publisher: Wiley
Date: 22-02-2019
DOI: 10.1002/QJ.3446
Publisher: Springer Science and Business Media LLC
Date: 15-02-2022
DOI: 10.1007/S00382-022-06176-6
Abstract: The East African ‘short rains’ in October–December (OND) exhibit large interannual variability. Drought and flooding are not unusual, and long-range rainfall forecasts can guide planning and preparedness for such events. Although seasonal forecasts based on dynamical models are making inroads, statistical models based on sea surface temperature (SST) precursors are still widely used, making it important to better understand the strengths and weaknesses of such models. Here we define a simple statistical forecast model, which is used as a tool to shed light on the dynamics that link SSTs and rainfall across time and space, as well as on why such models sometimes fail. Our model is a linear regression, where the August states of El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) predict about 40% of the short rains variability in 1950–2020. The forecast errors are traced back to the initial SSTs: too-wet (too-dry) forecasts are linked linearly to positive (negative) initial ENSO and IOD states in August. The link to the initial IOD state is mediated by changes in the IOD between August and OND, highlighting a physical mechanism for prediction busts. We also identify asymmetry and nonlinearity: when ENSO and/or the IOD are positive in August, the range and variance of OND forecast errors are larger than when the SST indices are negative. Upfront adjustments of predictions conditional on initial SSTs would have helped in some years with large forecast busts, such as the dry 1987 season during a major El Niño, for which the model erroneously predicts copious rainfall, but it would have exacerbated the forecast in the wet 2019 season, when the IOD was strongly positive and the model predicts too-dry conditions.
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-2928
Abstract: & & Predictions of the winter NAO and its small signal-to-noise ratio have been a matter of much discussion recently. Here we look at the problem from the perspective of 110-year-long historical hindcasts over the period 1901-2010 performed with ECMWF& #8217 s coupled model. Seasonal forecast skill of the NAO can undergo pronounced multidecadal variations: while skill drops in the middle of the century, the performance of the reforecasts recovers in the early twentieth century, suggesting that the mid-century drop in skill is not due to a lack of good observational data. We hypothesize instead that these changes in model predictability are linked to intrinsic changes of the coupled climate system.& & & & & The confidence of these predictions, and thus the signal-to-noise behaviour, also strongly depends on the specific hindcast period. Correlation-based measures like the Ratio of Predictable Components are shown to be highly sensitive to the strength of the predictable signal, implying that disentangling of physical deficiencies in the models on the one hand, and the effects of s ling uncertainty on the other hand, is difficult. These findings demonstrate that relatively short hindcasts are not sufficiently representative for longer-term behaviour and can lead to skill estimates that may not be robust in the future.& & & & See also: Weisheimer, A., D. Decremer, D. MacLeod, C. O'Reilly, T. Stockdale, S. Johnson and T.N. Palmer (2019). How confident are predictability estimates of the winter North Atlantic Oscillation?& Q. J. R. Meteorol. Soc.,& & strong& & /strong& , 140-159, doi:10.1002/qj.3446.& &
Publisher: Copernicus GmbH
Date: 11-05-2022
Publisher: Copernicus GmbH
Date: 12-07-2016
DOI: 10.5194/HESS-20-2737-2016
Abstract: Abstract. Soil moisture memory is a key component of seasonal predictability. However, uncertainty in current memory estimates is not clear and it is not obvious to what extent these are dependent on model uncertainties. To address this question, we perform a global sensitivity analysis of memory to key hydraulic parameters, using an uncoupled version of the H-TESSEL land surface model. Results show significant dependency of estimates of memory and its uncertainty on these parameters, suggesting that operational seasonal forecasting models using deterministic hydraulic parameter values are likely to display a narrower range of memory than exists in reality. Explicitly incorporating hydraulic parameter uncertainty into models may then give improvements in forecast skill and reliability, as has been shown elsewhere in the literature. Our results also show significant differences with previous estimates of memory uncertainty, warning against placing too much confidence in a single quantification of uncertainty.
Publisher: Copernicus GmbH
Date: 25-01-2021
DOI: 10.5194/NHESS-21-261-2021
Abstract: Abstract. Preparedness saves lives. Forecasts can help improve preparedness by triggering early actions as part of pre-defined protocols under the Forecast-based Financing (FbF) approach however it is essential to understand the skill of a forecast before using it as a trigger. In order to support the development of early-action protocols over Kenya, we evaluate the 33 heavy rainfall advisories (HRAs) issued by the Kenya Meteorological Department (KMD) during 2015–2019. The majority of HRAs warn counties which subsequently receive heavy rainfall within the forecast window. We also find a significant improvement in the advisory ability to anticipate flood events over time, with particularly high levels of skill in recent years. For instance actions with a 2-week lifetime based on advisories issued in 2015 and 2016 would have failed to anticipate nearly all recorded flood events in that period, whilst actions in 2019 would have anticipated over 70 % of the instances of flooding at the county level. When compared against the most significant flood events over the period which led to significant loss of life, all three such periods during 2018 and 2019 were preceded by HRAs, and in these cases the advisories accurately warned the specific counties for which significant impacts were recorded. By contrast none of the four significant flooding events in 2015–2017 were preceded by advisories. This step change in skill may be due to developing forecaster experience with synoptic patterns associated with extremes as well as access to new dynamical prediction tools that specifically address extreme event probability for ex le, KMD access to the UK Met Office Global Hazard Map was introduced at the end of 2017. Overall we find that KMD HRAs effectively warn of heavy rainfall and flooding and can be a vital source of information for early preparedness. However a lack of spatial detail on flood impacts and broad probability ranges limit their utility for systematic FbF approaches. We conclude with suggestions for making the HRAs more useful for FbF and outline the developing approach to flood forecasting in Kenya.
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-12207
Abstract: & & Dissemination of early warning information and effective preparedness are critical components in flood risk management and shape the dynamics of any successful early response. To enable efficient preparedness and an early response to hazards, early warning information should be simple, usable, and deployed through trusted sources.& & & & Successful management of floods are therefore dependent on clear and systematic communication structures which are, in turn, necessary to enable dissemination of such information. However, in many parts of Africa, flood event response is h ered by a lack of information on the inundation and potential exposure, with most large-scale systems limited to river flow forecasting, while local systems may lack coverage.& & & & In collaboration with the UK Foreign and Commonwealth Office, we have previously developed a method for fluvial flood inundation and exposure forecasting that combines hydrological forecasts from the Global Flood awareness systems (GLOFAS) with the LISFLOOD-FP global flood model. This was deployed in Mozambique to provide probabilistic inundation maps and exposure estimates to assist humanitarian response for cyclones Idai, Kenneth and Eloise.& & & & In this work, the flood models built with globally available datasets, and models augmented with local information, were evaluated for a series of uses and cases. Specifically, the 2020 fluvial flood event in the Nzoia basin in Kenya, the 2019 tropical cyclone flooding in Mozambique from cyclone Idai and recent pluvial flooding in the Zambian city of Lusaka.& & & & We discuss the potential and limitations of such information to inform efficient action and build resilient futures via co-production of forecasts in Kenya and the community learning lab component of the FRACTAL+ initiative in Lusaka.& &
Publisher: Copernicus GmbH
Date: 26-08-2020
Publisher: Copernicus GmbH
Date: 26-08-2020
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Dave MacLeod.