There is increasing pressure from stakeholders for highly localised climate change projections. A comprehensive assessment of climate model performance at the grid box scale in simulating recent ...change, however, is not available at present. Therefore, we compare observed changes in near-surface temperature, sea level pressure (SLP) and precipitation with simulations available from the Coupled Model Intercomparison Projects 3 and 5 (CMIP3 and CMIP5). In both multi-model datasets we find coherent areas of inconsistency between observed and simulated local trends per degree global warming in both temperature and SLP in the majority of models. Localised projections should thus take into account the possibility of regional biases shared across models. In contrast, simulated changes in precipitation are not significantly different from observations due to low signal-to-noise ratio of local precipitation changes. Therefore, recent regional rainfall change is likely not providing useful constraints for future projections as of yet. Comparing the two most recent sets of internationally coordinated climate model experiments, we find no indication of improvement in the models’ ability to reproduce local trends in temperature, SLP and precipitation.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This work presents a comprehensive intercomparison of different alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)—e.g. quantile mapping—to more ...sophisticated ensemble recalibration (RC) methods—e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account different aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Office-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with different skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods effectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value—with respect to the raw model outputs—beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly affects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Data assimilation is a promising approach to obtain climate reconstructions that are both consistent with observations of the past and with our understanding of the physics of the climate system as ...represented in the climate model used. Here, we investigate the use of ensemble square root filtering (EnSRF) - a technique used in weather forecasting - for climate reconstructions. We constrain an ensemble of 29 simulations from an atmosphere-only general circulation model (GCM) with 37 pseudo-proxy temperature time series. Assimilating spatially sparse information with low temporal resolution (semi-annual) improves the representation of not only temperature, but also other surface properties, such as precipitation and even upper air features such as the intensity of the northern stratospheric polar vortex or the strength of the northern subtropical jet. Given the sparsity of the assimilated information and the limited size of the ensemble used, a localisation procedure is crucial to reduce "overcorrection" of climate variables far away from the assimilated information.
Abstract Seasonal predictions have a great socioeconomic potential if they are reliable and skillful. In this study, we assess the prediction performance of SEAS5, version 5 of the seasonal ...prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF), over South America against homogenized station data. For temperature, we find the highest prediction performances in the tropics during austral summer, where the probability that the predictions correctly discriminate different observed outcomes is 70%. In regions lying to the east of the Andes, the predictions of maximum and minimum temperature still exhibit considerable performance, while farther to the south in Chile and Argentina the temperature prediction performance is low. Generally, the prediction performance of minimum temperature is slightly lower than for maximum temperature. The prediction performance of precipitation is generally lower and spatially and temporally more variable than for temperature. The highest prediction performance is observed at the coast and over the highlands of Colombia and Ecuador, over the northeastern part of Brazil, and over an isolated region to the north of Uruguay during DJF. In general, Niño-3.4 has a strong influence on both air temperature and precipitation in the regions where ECMWF SEAS5 shows high performance, in some regions through teleconnections (e.g., to the north of Uruguay). However, we show that SEAS5 outperforms a simple empirical prediction based on Niño-3.4 in most regions where the prediction performance of the dynamical model is high, thereby supporting the potential benefit of using a dynamical model instead of statistical relationships for predictions at the seasonal scale.
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The present paper is a follow-on of the work presented in Manzanas et al. (Clim Dyn 53(3–4):1287–1305, 2019) which provides a comprehensive intercomparison of alternatives for the post-processing ...(statistical adjustment, calibration and downscaling) of seasonal forecasts for a particularly interesting region, Southeast Asia. To answer the questions that were raised in the preceding work, apart from Bias Adjustment (BA) and ensemble Re-Calibration (RC) methods—which transform directly the variable of interest,—we include here more complex Perfect Prognosis (PP) and Model Outputs Statistics (MOS) downscaling techniques—which operate on a selection of large-scale model circulation variables linked to the local observed variable of interest. Moreover, we test the suitability of BA and PP methods for the post-processing of daily—not only seasonal—time-series, which are often needed in a variety of sectoral applications (crop, hydrology, etc.) or to compute specific climate indices (heat waves, fire weather index, etc.). In addition, we also undertake an assessment of the effect that observational uncertainty may have for statistical post-processing. Our results indicate that PP methods (and to a lesser extent MOS) are highly case-dependent and their application must be carefully analyzed for the region/season/application of interest, since they can either improve or degrade the raw model outputs. Therefore, for those cases for which the use of these methods cannot be carefully tested by experts, our overall recommendation would be the use of BA methods, which seem to be a safe, easy to implement alternative that provide competitive results in most situations. Nevertheless, all methods (including BA ones) seem to be sensitive to observational uncertainty, especially regarding the reproduction of extremes and spells. For MOS and PP methods, this issue can even lead to important regional differences in interannual skill. The lessons learnt from this work can substantially benefit a wide range of end-users in different socio-economic sectors, and can also have important implications for the development of high-quality climate services.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Subseasonal predictions bridge the gap between medium‐range weather forecasts and seasonal climate predictions. This time horizon is of crucial importance for many planning purposes, including energy ...production and agriculture. The verification of such predictions is normally done for areal averages of upper‐air parameters. Only few studies exist that verify the forecasts for surface parameters with observational stations, although this is crucial for real‐world applications, which often require such predictions at specific surface locations. With this study we provide an extensive station‐based verification of subseasonal forecasts against 1,637 ground based observational time series across Europe. Twenty years of temperature and precipitation reforecasts of the European Centre for Medium‐Range Weather Forecasts Integrated Forecasting System are used to analyze the period of April 1995 to March 2014. A lead time and seasonally dependent bias correction is performed to correct the daily temperature and precipitation forecasts at all stations individually. Two bias correction techniques are compared, a mean debiasing method and a quantile mapping approach. Commonly used skill scores characterizing different aspects of forecast quality are computed for weekly aggregated forecasts with lead times of 5–32 days. Overall, promising skill is found for temperature in all seasons except spring. Temperature forecasts tend to show higher skill in Northern Europe and in particular around the Baltic Sea, and in winter. Bias correction is shown to be essential in enhancing the forecast skill in all four weeks for most of the stations and for both variables with QM generally performing better.
Key Points
We provide a station based verification of downscaled and bias‐corrected subseasonal temperature and precipitation predictions for entire Europe
Promising skill is found for weekly mean temperature up to 19‐24 days lead time, but limited to lead days 5‐11 for precipitation
Seasonal and spatial variations in the skill of the forecasts are discussed in the context of subseasonal to seasonal predictions
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Background and Aims
Climate is a key consideration for winegrowers, so information regarding projected climate change and the relative global impacts are of great interest.
Methods and Results
This ...climate analogue approach compares and contrasts future to current climate among the regions. Warming was projected for all regions, with greater warming in Northern Hemisphere continental regions and less for Southern Hemisphere and coastal regions. Projections of annual precipitation varied, with the median result from the range of models indicating a wetter climate for higher latitude regions, such as New Zealand, Mosel Valley and North Oregon, and the Chinese region in this study, while Southern European, Australian and South African winegrowing regions had a projected drier climate. The median model result for winter precipitation indicated a projected decrease for sites in Chile, Greece, Australia and Spain, with other European and American sites experiencing a slight increase in winter precipitation. Shandong, China, was the only region in this study projected to experience increased summer precipitation.
Conclusion
Future temperature and precipitation projections were made for 23 winegrowing regions worldwide and compared using a climate analogue approach.
Significance of the Study
An estimated future temperature and precipitation climatology centred on 2030 and 2070 for 23 winegrowing regions worldwide is made using results from 23 global climate models. Using these climatologies, along with consideration of length of the planning horizon, legal limitations and inter‐regional climate variability, climate analogues can be described. For future planning, winegrape cultivars better suited to the projected climate may then be selected from the range currently growing in identified analogue regions. Alternatively, climatically optimum sites can be identified for growing particular cultivars in future conditions.
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We present a vertically resolved zonal mean monthly mean global ozone data set spanning the period 1901 to 2007, called HISTOZ.1.0. It is based on a new approach that combines information from an ...ensemble of chemistry climate model (CCM) simulations with historical total column ozone information. The CCM simulations incorporate important external drivers of stratospheric chemistry and dynamics (in particular solar and volcanic effects, greenhouse gases and ozone depleting substances, sea surface temperatures, and the quasi-biennial oscillation). The historical total column ozone observations include ground-based measurements from the 1920s onward and satellite observations from 1970 to 1976. An off-line data assimilation approach is used to combine model simulations, observations, and information on the observation error. The period starting in 1979 was used for validation with existing ozone data sets and therefore only ground-based measurements were assimilated. Results demonstrate considerable skill from the CCM simulations alone. Assimilating observations provides additional skill for total column ozone. With respect to the vertical ozone distribution, assimilating observations increases on average the correlation with a reference data set, but does not decrease the mean squared error. Analyses of HISTOZ.1.0 with respect to the effects of El Niño–Southern Oscillation (ENSO) and of the 11 yr solar cycle on stratospheric ozone from 1934 to 1979 qualitatively confirm previous studies that focussed on the post-1979 period. The ENSO signature exhibits a much clearer imprint of a change in strength of the Brewer–Dobson circulation compared to the post-1979 period. The imprint of the 11 yr solar cycle is slightly weaker in the earlier period. Furthermore, the total column ozone increase from the 1950s to around 1970 at northern mid-latitudes is briefly discussed. Indications for contributions of a tropospheric ozone increase, greenhouse gases, and changes in atmospheric circulation are found. Finally, the paper points at several possible future improvements of HISTOZ.1.0.
Model evaluation is an important tool to help rate confidence in climate model simulations. This can add to the overall confidence assessment for future projections of the Australian climate. ...Additionally it can highlight significant model deficiencies that may affect the selection of a subset of models for use in impact assessment. Here we present results from an extensive model evaluation undertaken as part of the Natural Resource Management (NRM) Project in order to inform the newest set of climate change projections for Australia. The assessment covers mean climate skill over Australia as well as variability measures and teleconnections from up to 47 CMIP5 models and 23 CMIP3 models (for comparison where appropriate). Additionally, the skill in representing important climate features such as MJO, SAM, blocking and cut-off lows are also reviewed. Selected extremes are evaluated as well as simulations of two different types of downscaling simulations used within the NRM project. Finally, an attempt is made to synthesise this information in order to highlight a small group of CMIP5 models which show consistent deficiencies in representing the Australian climate and its features.
Statistical postprocessing of medium-range weather forecasts is an important component of modern forecasting systems. Since the beginning of modern data science, numerous new postprocessing methods ...have been proposed, complementing an already very diverse field. However, one of the questions that frequently arises when considering different methods in the framework of implementing operational postprocessing is the relative performance of the methods for a given specific task. It is particularly challenging to find or construct a common comprehensive dataset that can be used to perform such comparisons. Here, we introduce the first version of EUPPBench (EUMETNET postprocessing benchmark), a dataset of time-aligned forecasts and observations, with the aim to facilitate and standardize this process. This dataset is publicly available at
https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark (31 December 2022) and on Zenodo (https://doi.org/10.5281/zenodo.7429236, Demaeyer, 2022b and
https://doi.org/10.5281/zenodo.7708362, Bhend et al., 2023). We provide examples showing how to download and use the data, we propose a set of evaluation methods, and we perform a first benchmark of several methods for the correction of 2 m temperature forecasts.
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