ORCID Profile
0000-0002-7231-6974
Current Organisations
European Centre for Medium-Range Weather Forecasts
,
National Centre for Atmospheric Science
,
University of Oxford
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Publisher: Springer Science and Business Media LLC
Date: 20-09-2021
DOI: 10.1038/S43247-021-00268-7
Abstract: Internal climate variability will play a major role in determining change on regional scales under global warming. In the extratropics, large-scale atmospheric circulation is responsible for much of observed regional climate variability, from seasonal to multidecadal timescales. However, the extratropical circulation variability on multidecadal timescales is systematically weaker in coupled climate models. Here we show that projections of future extratropical climate from coupled model simulations significantly underestimate the projected uncertainty range originating from large-scale atmospheric circulation variability. Using observational datasets and large ensembles of coupled climate models, we produce synthetic ensemble projections constrained to have variability consistent with the large-scale atmospheric circulation in observations. Compared to the raw model projections, the synthetic observationally-constrained projections exhibit an increased uncertainty in projected 21st century temperature and precipitation changes across much of the Northern extratropics. This increased uncertainty is also associated with an increase of the projected occurrence of future extreme seasons.
Publisher: Wiley
Date: 02-04-2018
DOI: 10.1002/ASL.815
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: Wiley
Date: 07-2017
DOI: 10.1002/QJ.3094
Publisher: American Geophysical Union (AGU)
Date: 04-08-2013
DOI: 10.1029/2022GL098568
Abstract: This study presents an approach to provide seamless climate information by concatenating decadal climate predictions and climate projections in time. Results for near‐surface air temperature over 29 regions indicate that such an approach has potential to provide meaningful information but can also introduce significant inconsistencies. Inconsistencies are often most pronounced for relatively extreme quantiles of the CMIP6 multi‐model ensemble distribution, whereas they are generally smaller and mostly insignificant for quantiles close to the median. The regions most affected are the North Atlantic, Greenland and Northern Europe. Two potential ways to reduce inconsistencies are discussed, including a simple calibration method and a weighting approach based on model performance. Calibration generally reduces inconsistencies but does not eliminate all of them. The impact of model weighting is minor, which is found to be linked to the small size of the decadal climate prediction ensemble, which in turn limits the applicability of that method.
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: American Geophysical Union (AGU)
Date: 18-05-2022
DOI: 10.1029/2022GL097885
Abstract: In order to explore temporal changes of predictability of El Niño Southern Oscillation (ENSO), a novel set of global biennial climate reforecasts for the historical period 1901–2010 has been generated using a modern initialized coupled forecasting system. We find distinct periods of enhanced long‐range skill at the beginning and at the end of the twentieth century, and an extended multi‐decadal epoch of reduced skill during the 1930s–1950s. Once the forecast skill extends beyond the first spring barrier, the predictability limit is much enhanced and our results provide support for the feasibility of skillful ENSO forecasts up to 18 months. Changes in the mean state, variability ( litude), persistence, seasonal cycle and predictability suggest that multi‐decadal variations in the dynamical characteristics of ENSO rather than the data coverage and quality of the observations have primarily driven the reported non‐monotonic skill modulations.
Publisher: Wiley
Date: 22-02-2019
DOI: 10.1002/QJ.3446
Publisher: Copernicus GmbH
Date: 17-02-2016
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: American Meteorological Society
Date: 03-2023
Abstract: Despite the growing demand for long-range ENSO predictions beyond 1 year, quantifying the skill at these lead times remains limited. This is partly due to inadequate long records of seasonal reforecasts that make skill estimates of irregular ENSO events quite challenging. Here, we investigate ENSO predictability and the dependency of prediction skill on the ENSO cycle using 110 years of 24-month-long 10-member ensemble reforecasts from ECMWF’s coupled model (SEAS5-20C) initialized on 1 November and 1 May during 1901–2010. Results show that Niño-3.4 SST can be skillfully predicted up to ∼18 lead months when initialized on 1 November, but skill drops at ∼12 lead months for May starts that encounter the boreal spring predictability barrier in year 2. The skill beyond the first year is highly conditioned to the phase of ENSO: Forecasts initialized at peak El Niño are more skillful in year 2 than those initialized at peak La Niña, with the transition to La Niña being more predictable than to El Niño. This asymmetry is related to the subsurface initial conditions in the western equatorial Pacific: peak El Niño states evolving into La Niña are associated with strong upper-ocean heat discharge of the western Pacific, the memory of which stays beyond 1 year. In contrast, the western Pacific recharged state associated with La Niña is usually weaker and shorter-lived, being a weaker preconditioner for subsequent El Niño, the year after. High prediction skill of ENSO events beyond 1 year provides motivation for extending the lead time of operational seasonal forecasts up to 2 years.
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 Antje Weisheimer.