Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community

Regional Environmental Change, Год журнала: 2020, Номер 20(2)

Опубликована: Апрель 23, 2020

Abstract The European CORDEX (EURO-CORDEX) initiative is a large voluntary effort that seeks to advance regional climate and Earth system science in Europe. As part of the World Climate Research Programme (WCRP) - Coordinated Regional Downscaling Experiment (CORDEX), it shares broader goals providing model evaluation projection framework improving communication with both General Circulation Model (GCM) data user communities. EURO-CORDEX oversees design coordination ongoing ensembles projections unprecedented size resolution (0.11° EUR-11 0.44° EUR-44 domains). Additionally, inclusion empirical-statistical downscaling allows investigation much larger multi-model ensembles. These complementary approaches provide foundation for scientific studies within research community others. value ensemble shown via numerous peer-reviewed its use development services. Evaluations also show benefits higher resolution. However, significant challenges remain. To further understanding, two flagship pilot (FPS) were initiated. first investigates local-regional phenomena at convection-permitting scales over central Europe Mediterranean collaboration Med-CORDEX community. second impacts land cover changes on across spatial temporal scales. Over coming years, looks forward closer other communities, new advances, supporting international initiatives such as IPCC reports, continuing basis adaptation

Язык: Английский

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An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment

International Journal of Climatology, Год журнала: 2018, Номер 39(9), С. 3750 - 3785

Опубликована: Март 23, 2018

VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process‐based, etc.). Here we describe the participating methods and first results from experiment, using “perfect” reanalysis (and reanalysis‐driven regional model (RCM)) predictors assess intrinsic performance of precipitation temperatures over a set 86 stations representative main climatic regions in Europe. This study constitutes largest most comprehensive date intercomparison statistical methods, covering three common (perfect prognosis, output statistics—including bias correction—and weather generators) with total 50 techniques. Overall, greatly improve (reanalysis or RCM) raw biases no approach technique seems be superior general, because there large method‐to‐method variability. The factors influencing are seasonal calibration (e.g., moving window) their stochastic nature. particular used also play important role cases where comparison was possible, both strength predictor–predictand link, indicating local variability explained. However, present cannot give conclusive assessment skill simulate future climates, further experiments will soon performed framework EURO‐CORDEX initiative (where activities have merged follow on). Finally, research transparency reproducibility has been major concern substantive steps taken. In particular, necessary data run provided at http://www.value‐cost.eu/data available portal investigation: http://www.value‐cost.eu/validationportal .

Язык: Английский

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Configuration and intercomparison of deep learning neural models for statistical downscaling

Geoscientific model development, Год журнала: 2020, Номер 13(4), С. 2109 - 2124

Опубликована: Апрель 28, 2020

Abstract. Deep learning techniques (in particular convolutional neural networks, CNNs) have recently emerged as a promising approach for statistical downscaling due to their ability learn spatial features from huge spatiotemporal datasets. However, existing studies are based on complex models, applied case and using simple validation frameworks, which makes proper assessment of the (possible) added value offered by these difficult. As result, models usually seen black boxes, generating distrust among climate community, particularly in change applications. In this paper we undertake comprehensive deep continental-scale downscaling, building VALUE framework. particular, different CNN increasing complexity downscale temperature precipitation over Europe, comparing them with few standard benchmark methods (linear generalized linear models) been traditionally used purpose. Besides analyzing adequacy components topologies, also focus extrapolation capability, critical point potential application studies. To do this, use warm test period surrogate possible future conditions. Our results show that, while CNNs is mostly limited reproduction extremes temperature, outperform classic ones most aspects considered. This overall good performance, together fact that they can be suitably large regions (e.g., continents) without worrying about being considered predictors, foster approaches international initiatives such Coordinated Regional Climate Downscaling Experiment (CORDEX).

Язык: Английский

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Deep Learning for Daily Precipitation and Temperature Downscaling

Water Resources Research, Год журнала: 2021, Номер 57(4)

Опубликована: Апрель 1, 2021

Abstract Downscaling is a critical step to bridge the gap between large‐scale climate information and local‐scale impact assessment. This study presents novel deep learning approach: Super Resolution Deep Residual Network (SRDRN) for downscaling daily precipitation temperature. approach was constructed based on an advanced convolutional neural network with residual blocks batch normalizations. The data augmentation technique utilized address overfitting that due highly imbalanced nonprecipitation days sparse extremes. Synthetic experiments were designed downscale maximum/minimum temperature from coarse resolutions (25, 50, 100 km) high resolution (4 km). results showed that, during validation period, SRDRN not only captured spatial temporal patterns remarkably well, but also reproduced both extremes in different locations time at local scale. Through transfer learning, trained model one region directly applied another environment, notable improvement compared classic statistical methods. outstanding performance of stemmed its ability fully extract features without suffering degradation issues incorporations blocks, normalizations, augmentations. thus powerful tool can potentially be leveraged any hydrologic, climate, earth system data.

Язык: Английский

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Mean and extreme temperatures in a warming climate: EURO CORDEX and WRF regional climate high-resolution projections for Portugal

Climate Dynamics, Год журнала: 2018, Номер 52(1-2), С. 129 - 157

Опубликована: Фев. 15, 2018

Язык: Английский

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Heat waves in Portugal: Current regime, changes in future climate and impacts on extreme wildfires

The Science of The Total Environment, Год журнала: 2018, Номер 631-632, С. 534 - 549

Опубликована: Март 18, 2018

Язык: Английский

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Estimating daily meteorological data and downscaling climate models over landscapes

Environmental Modelling & Software, Год журнала: 2018, Номер 108, С. 186 - 196

Опубликована: Авг. 4, 2018

Язык: Английский

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Comparison of statistical downscaling methods with respect to extreme events over Europe: Validation results from the perfect predictor experiment of the COST Action VALUE

International Journal of Climatology, Год журнала: 2018, Номер 39(9), С. 3846 - 3867

Опубликована: Март 6, 2018

Credible information about the properties and changes of extreme events on regional local scales is prime importance in context future climate change. Within EU‐COST Action VALUE a comprehensive validation framework for downscaling methods has been developed. Here we present results extremes temperature precipitation from perfect predictor experiment that uses reanalysis‐based predictors to isolate skill. The raw reanalysis output reveals there mostly large bias with respect index values at considered stations across Europe, clearly pointing necessity downscaling. performance closely linked their specific structure setup. All using parametric distributions require non‐standard correctly represent marginal aspects extremes. Also, much improved by explicitly including seasonal component, particularly case precipitation. With best found model statistics (MOS), weather generators (WGs) as well prognosis (PP) analogues. Spell‐length‐related are assessed MOS WGs, spell‐length‐related PP skill transfer functions varies strongly depends index, region season considered.

Язык: Английский

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Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment

Geoscientific model development, Год журнала: 2020, Номер 13(3), С. 1711 - 1735

Опубликована: Апрель 1, 2020

Abstract. The increasing demand for high-resolution climate information has attracted growing attention to statistical downscaling (SDS) methods, due in part their relative advantages and merits as compared dynamical approaches (based on regional model simulations), such much lower computational cost fitness purpose many local-scale applications. As a result, plethora of SDS methods is nowadays available scientists, which motivated recent efforts comprehensive evaluation, like the VALUE initiative (http://www.value-cost.eu, last access: 29 March 2020). systematic intercomparison large number techniques undertaken VALUE, them independently developed by different authors modeling centers variety languages/environments, shown compelling need new tools allowing application within an integrated framework. In this regard, downscaleR R package covers most popular (model output statistics – including so-called “bias correction” perfect prognosis) state-of-the-art techniques. It been conceived work primarily with daily data can be used framework both seasonal forecasting change studies. Its full integration climate4R (Iturbide et al., 2019) makes possible development end-to-end applications, from retrieval building, validation, prediction, bringing scientists practitioners unique development. article main features are showcased through replication some results obtained placing emphasis technically complex stages perfect-prognosis calibration (predictor screening, cross-validation, selection) that accomplished simple commands extremely flexible tuning, tailored needs users requiring easy interface levels experimental complexity. open-source framework, freely necessary scripts fully replicate experiments included paper also provided companion notebook.

Язык: Английский

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Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought

Hydrology and earth system sciences, Год журнала: 2021, Номер 25(6), С. 3493 - 3517

Опубликована: Июнь 21, 2021

Abstract. General circulation models (GCMs) are the primary tools for evaluating possible impacts of climate change; however, their results coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations. Downscaling bias correction model outputs is thus required local applications. Apart from computationally intensive strategy dynamical downscaling, statistical downscaling offers a relatively straightforward solution by establishing relationships between small- large-scale variables. This study compares four methods (BC), change factor mean (CFM), quantile perturbation (QP) an event-based weather generator (WG) assess impact on drought end 21st century (2071–2100) relative baseline period 1971–2000 station Uccle located Belgium. A set drought-related aspects analysed, i.e. dry day frequency, spell duration total precipitation. The applied 28-member ensemble Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs, each forced future scenarios SSP1–2.6, SSP2–4.5, SSP3–7.0 SSP5–8.5. 25-member CanESM5 GCM also used significance signals comparison internal variability climate. performance reveals that QP method outperforms others reproducing magnitude monthly pattern observed indicators. While all good agreement precipitation, differ quite largely frequency length spells. Using methods, projected increase significantly summer months, with up 19 % At same time, precipitation decrease 33 these months. Total increases winter, as it driven significant intensification extreme rather than change. Lastly, spells 9 %.

Язык: Английский

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