CSTools: the MEDSCOPE Toolbox for Climate Forecasts postprocessing

<p>Climate forecasts need to be postprocessed to obtain user-relevant climate information, to develop and implement strategies of adaptation to climate variability and to trigger decisions. Several postprocessing methods are gathered into CSTools (short for Climate Service Tools) for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. </p><p>Besides an overview of the methods and documentation available in CSTools, a practical example is demonstrated. The objective of this practical example is to postprocess a seasonal forecast with a set of CSTools functions in order to obtain the required data to produce forecasts of mountain snow resources. Quantile mapping bias-correction and RainFARM stochastic downscaling methods are applied to raw seasonal forecast daily precipitation data to derive 1 km resolution fields. Bias-adjusted and downscaled precipitation data are then employed to drive a snow model, SNOWPACK, and generate snow depth seasonal forecasts at selected high-elevation sites in North-Western Italian Alps. </p><p>The computational resources required by CSTools to process the forecasts will be discussed. This assessment is relevant given the memory requirements for the use case: while seasonal forecast data occupies ~10MB (8 x 8 grid cells, 215 forecast time steps for 30 different initializations with 25 members each), the data post-processed reaches ~1TB (the RainFARM downscaling requires a refinement factor 100 for the SNOWPACK model increasing the spatial resolution to 800 x 800 grid cells and creating 10 stochastic realizations for each ensemble member). In addition to one strategy using conventional loops, startR is introduced as an efficient alternative. startR is an R package that allows implementing the MapReduce paradigm, i.e. chunking the data and processing them either locally or remotely on high-performance computing systems, leveraging multi-node and multi-core parallelism where possible.</p>

New Approach for Bias Correction and Stochastic Downscaling of Future Projections for Daily Mean Temperatures to a High-Resolution Grid

In applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of subgrid variability and the spatial and temporal dependence at the finer scale. Here, a postprocessing procedure for temperature projections is proposed that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In the first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Second, residual space–time dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with an appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study in which projections of two regional climate models from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) ensemble are bias corrected and downscaled to a 1 km × 1 km grid in the Trøndelag area of Norway. A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time when compared with empirical quantile mapping.

Stochastic downscaling of precipitation in complex orography: a simple method to reproduce a realistic fine-scale climatology

Stochastic rainfall downscaling methods usually do not take into account orographic effects or local precipitation features at spatial scales finer than those resolved by the large-scale input field. For this reason they may be less reliable in areas with complex topography or with sub-grid surface heterogeneities. Here we test a simple method to introduce realistic fine-scale precipitation patterns into the downscaled fields, with the objective of producing downscaled data more suitable for climatological and hydrological applications as well as for extreme event studies. The proposed method relies on the availability of a reference fine-scale precipitation climatology from which corrective weights for the downscaled fields are derived. We demonstrate the method by applying it to the Rainfall Filtered Autoregressive Model (RainFARM) stochastic rainfall downscaling algorithm. The modified RainFARM method is tested focusing on an area of complex topography encompassing the Swiss Alps, first, in a “perfect-model experiment” in which high-resolution (4 km) simulations performed with the Weather Research and Forecasting (WRF) regional model are aggregated to a coarser resolution (64 km) and then downscaled back to 4 km and compared with the original data. Second, the modified RainFARM is applied to the E-OBS gridded precipitation data (0.25∘ spatial resolution) over Switzerland, where high-quality gridded precipitation climatologies and accurate in situ observations are available for comparison with the downscaled data for the period 1981–2010. The results of the perfect-model experiment confirm a clear improvement in the description of the precipitation distribution when the RainFARM stochastic downscaling is applied, either with or without the implemented orographic adjustment. When we separately analyze grid points with precipitation climatology higher or lower than the median calculated over the neighboring grid points, we find that the probability density function (PDF) of the real precipitation is better reproduced using the modified RainFARM rather than the standard RainFARM method. In fact, the modified method successfully assigns more precipitation to areas where precipitation is on average more abundant according to a reference long-term climatology. The results of the E-OBS downscaling show that the modified RainFARM introduces improvements in the representation of precipitation amplitudes. While for low-precipitation areas the downscaled and the observed PDFs are in good agreement, for high-precipitation areas residual differences persist, mainly related to known E-OBS deficiencies in properly representing the correct range of precipitation values in the Alpine region. The downscaling method discussed is not intended to correct the bias which may be present in the coarse-scale data, so possible biases should be adjusted before applying the downscaling procedure.

An emergent transition time-scale in the atmosphere and its implications to global-averaged precipitation control mechanisms, time-series reconstruction and stochastic downscaling

Detrended Cross-Correlation Analysis (DCCA) revealed an emergent transition in non-periodic (deseasonalized) atmospheric variability at time-scales ~1-year. At multi-year time-scales (i) ρSST,Tland ~ 0.6 (i.e. the correlation been global-averaged sea surface temperature, SST, and 2-meter air temperature averaged over global-land, Tland); (ii) Clausius-Clapeyron relationship becomes the dominant control of global-averaged precipitable water vapor (W), with ρW,T2m ≈ ρW,SST ~ 0.9; (iii) atmospheric radiative fluxes, specifically the surface downwelling longwave radiative flux (DLR), become a key constraint for global-mean precipitation (P) variability (ρP,Ratm ≈ ρP,DLR ~ −0.8); (iv) cloud effects are negligible in (iii), and clear-sky DLR becomes a dominant P constraint; and (v) ρP,T2m and ρP,SST displayed significant multi-year correlations, although with large spread amongst different datasets (~ 0.4 to ~ 0.7). Result (v) provides a new perspective into the well-known uncertainties climate models associated with the dynamical component of precipitation. At sub-yearly time-scales all correlations underlying these five results decrease abruptly towards negligible values. The relevance and validity of this multi-scale structure is demonstrated by three reconstructed P time-series at 2-year resolution, two relying on clear-sky DLR constraints and one based on P-SST correlation. These simple models, particularly one based on clear-sky DLR, were able to reproduce observed P anomaly time-series with similar accuracy to a (uncoupled) atmospheric model (ERA-20CM) and two climate reanalysis (ERA-20C and 20CR). The idealized models aren't applicable at sub-yearly time-scales, where the underlying correlations become negligible. However, monthly P probability density functions (PDFs) were derived by stochastic downscaling of reconstructed P, leveraging on scale-invariant properties, outperforming the statistics simulated by ERA-20C, 20CR and ERA-20CM.

Stochastic downscaling of precipitation in complex orography: a simple method to reproduce a realistic fine-scale climatology

Stochastic rainfall downscaling methods usually do not take into account orographic effects or local precipitation features at spatial scales finer than those resolved by the large-scale input field. For this reason they may be less reliable in areas with complex topography or with sub-grid surface heterogeneities. Here we test a simple method to introduce realistic fine-scale precipitation patterns into the downscaled fields, with the objective of producing downscaled data more suitable for climatological and hydrological applications as well as for extreme events studies. The proposed method relies on the availability of a reference fine-scale precipitation climatology from which corrective weights for the downscaled fields are derived. We demonstrate the method by applying it to the Rainfall Filtered AutoRegressive Model (RainFARM) stochastic rainfall downscaling algorithm. The modified RainFARM method has been tested focusing on an area of complex topography encompassing the Swiss Alps, first, in a perfect model experiment in which high resolution (4 km) simulations performed with the Weather Research and Forecasting (WRF) regional model are aggregated to a coarser resolution (64 km) and then downscaled back to 4 km and compared with the original data. Second, the modified RainFARM is applied to the E-OBS gridded precipitation data (0.25 degrees spatial resolution) over Switzerland, where high-quality gridded precipitation climatologies and accurate in-situ observations are available for comparison with the downscaled data for the period 1981–2010. The results of the perfect model experiment confirm a clear improvement in the description of the precipitation distribution when the RainFARM stochastic downscaling is applied, either with or without the implemented orographic adjustment. When we separately analyze areas with precipitation climatology higher or lower than the median calculated over all the points in the domain, we find that the Probability Density Function (PDF) of the real precipitation is better reproduced using the modified RainFARM rather than the standard RainFARM method. In fact, the modified method successfully assigns more precipitation to areas where precipitation is on average more abundant according to a reference long-term climatology. The results of the E-OBS downscaling show that the modified RainFARM introduces improvements in the representation of precipitation amplitudes. While for low-precipitation areas the downscaled and the observed PDFs are in excellent agreement, for high-precipitation areas residual differences persist, mainly related to known E-OBS deficiencies in properly representing the correct range of precipitation values in the Alpine region. The downscaling method discussed is not intended to correct the bias eventually present in the coarse-scale data, so possible biases should be adjusted before applying the downscaling procedure.