Empirical Statistical Downscaling Research Articles

STAR‐ESDM: A Generalizable Approach to Generating High‐Resolution Climate Projections Through Signal Decomposition

AbstractHigh‐resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR‐ESDM) is a computationally‐efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi‐Gaussian time series, gridded or point‐based, into multi‐decadal long‐term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long‐term predictor trends are bias‐corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high‐resolution bias‐corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR‐ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high‐resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1‐in‐1000 hottest and coldest temperatures are <0.5°C and biases in the 1‐in‐1000 wet day precipitation amounts are <5 mm/day. As climate impacts intensify, STAR‐ESDM represents a significant advance in generating consistent high‐resolution projections to comprehensively assess climate risk and optimize resilience globally.

PyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information

Abstract. The nature and severity of climate change impacts vary significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies. This demand has led to an increase in the application of empirical-statistical downscaling (ESD) models to general circulation model (GCM) simulations of future climate. In contrast to dynamical downscaling, the perfect prognosis ESD (PP-ESD) approach has several benefits, including low computation costs, the prevention of the propagation of GCM-specific errors, and high compatibility with different GCMs. Despite their advantages, the use of ESD models and the resulting data products is hampered by (1) the lack of accessible and user-friendly downscaling software packages that implement the entire downscaling cycle, (2) difficulties reproducing existing data products and assessing their credibility, and (3) difficulties reconciling different ESD-based predictions for the same region. We address these issues with a new open-source Python PP-ESD modeling framework called pyESD. pyESD implements the entire downscaling cycle, i.e., routines for data preparation, predictor selection and construction, model selection and training, evaluation, utility tools for relevant statistical tests, visualization, and more. The package includes a collection of well-established machine learning algorithms and allows the user to choose a variety of estimators, cross-validation schemes, objective function measures, and hyperparameter optimization in relatively few lines of code. The package is well-documented, highly modular, and flexible. It allows quick and reproducible downscaling of any climate information, such as precipitation, temperature, wind speed, or even short-term glacier length and mass changes. We demonstrate the use and effectiveness of the new PP-ESD framework by generating weather-station-based downscaling products for precipitation and temperature in complex mountainous terrain in southwestern Germany. The application example covers all important steps of the downscaling cycle and different levels of experimental complexity. All scripts and datasets used in the case study are publicly available to (1) ensure the reproducibility and replicability of the modeled results and (2) simplify learning to use the software package.

Global climate change and planet protection: Multinational adaptation

Adapting to climate change requires all hands to be on desk by many actors in the society. Of recent, major research focus has been on governments, communities and the third sector as key actors in the adaptation process. The objective of this research is to assess the extent global climate change has relationship with planet protection in a multinational adaptation using a family of approaches collectively described as empirical statistical downscaling as a methodology. Yet, there is a growing emphasis internationally on understanding the role of, and the need to engage businesses in adaptation looking at their potentials to finance projects, as well as develop and deploy technologies and innovative solutions, and enhance the scale, and cost-effectiveness of certain adaptation criterion. In several occasions, many multinational corporations (MNCs) are purportedly beginning to take steps to adapt their operations to climate change. Some stated reasons for their engagement which include minimising potential impacts on their supply chains, improving resource efficiency, enhancing the production and use of sustainable raw materials, and supporting customers’, suppliers’ and communities’ efforts to adapt to climate change. In conclusion, there is a paucity of work analysing adaptation actions by MNCs, their motivations and contribution to broader adaptation and climate resilient development efforts, as well as possible instances of maladaptation. We apply a three-tier framework on drivers, responses and outcomes to examine the state of knowledge according to recent literature on private sector and MNC adaptation to climate change. The research recommends building adaptive capacity of the MNCs, its supply chain and of the community where it operates; to transferring as well as adopting technologies and acting on opportunities related to adaptation.

Decadal variability in the austral summer precipitation over the Central Andes: Observations and the empirical‐statistical downscaling model

AbstractThe decadal variability in summer precipitation over the Central Andes (10°–30°S) is investigated from 1921 to 2010 using low‐pass filtered time series of the central and eastern El Niño–Southern Oscillation (ENSO) Pacific (C and E) indices, the South Pacific Convergence Zone (SPCZ) index, the Atlantic SST indices, Atlantic Multidecadal Oscillation (AMO) index, North Atlantic Oscillation (NAO) index, and ERA‐20C reanalysis. Additionally, an empirical‐statistical downscaling (ESD) model was built. A rotated empirical orthogonal function (REOF) analysis shows that the first leading mode of precipitation (RPC1) represents 38.2% of the total decadal variance. RPC2, RCP3, and RPC4 represent 18.8, 12.8, and 9.7% of the total decadal variance, respectively. Furthermore, RPC1 features highest loadings over most of the region. RPC2 features a dipole of highest loadings over the southernmost Bolivian Altiplano and lowest loadings over the northwestern Argentinian Andes. Conversely, RPC3 presents highest loadings over the eastern‐central Bolivian Altiplano and northwestern Argentinian Andes. RPC4 features highest loadings over the southern Bolivian Andes. RPC1 and RPC3 wet summers are associated with moisture transport from the Amazon basin, but RPC1 features the strengthening upper‐level Bolivian high‐Nordeste low system over South America. Conversely, RPC2 and RPC4 wet summers are associated with local processes induced by southward displacement of the South Atlantic Convergence Zone and warm sea surface temperature (SST) anomalies over the Indian Ocean, respectively. According to the ESD model, the decadal variability in the central and eastern Pacific (CP and EP) and Atlantic Ocean reproduces the decadal component of the DJF precipitation over most of the Central Andes.

Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach

Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). In the longer term, the final aim of this tool is to enlarge the high-resolution RCM simulation ensembles at low cost to explore better the various sources of projection uncertainty at local scale. Using a neural network, we build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12 km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM, particularly how the RCM refines the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a substantial computational benefit of running the emulator rather than the RCM, since training the emulator takes about 2 h on GPU, and the prediction takes less than a minute. However, further work is needed to improve the reproduction of some temperature extremes, the climate change intensity and extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

A Comparison between Analog Ensemble and Convolutional Neural Network Empirical-Statistical Downscaling Techniques for Reconstructing High-Resolution Near-Surface Wind

Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamical downscaling in representing a high-resolution regional climate. Two distinct strategies of ESD are employed here to reconstruct near-surface winds in a region of rugged terrain. ESD is used to reconstruct the innermost grid of a multiply nested mesoscale model framework for regional climate downscaling. An analog ensemble (AnEn) and a convolutional neural network (CNN) are compared in their ability to represent near-surface winds in the innermost grid in lieu of dynamical downscaling. Downscaling for a 30 year climatology of 10 m April winds is performed for southern MO, USA. Five years of training suffices for producing low mean absolute error and bias for both ESD techniques. However, root-mean-squared error is not significantly reduced by either scheme. In the case of the AnEn, this is due to a minority of cases not producing a satisfactory representation of high-resolution wind, accentuating the root-mean-squared error in spite of a small mean absolute error. Homogeneous comparison shows that the AnEn produces smaller errors than the CNN. Though further tuning may improve results, the ESD techniques considered here show that they can produce a reliable, computationally inexpensive method for reconstructing high-resolution 10 m winds over complex terrain.

Evaluation of statistical downscaling methods for climate change projections over Spain: Future conditions with pseudo reality (transferability experiment)

AbstractThe Spanish Meteorological Agency (AEMET) is responsible for the elaboration of downscaled climate projections over Spain to feed the Second National Plan of Adaptation to Climate Change (PNACC‐2) and this is the last of three papers aimed to evaluate and intercompare five empirical/statistical downscaling (ESD) methods developed at AEMET: (a) Analog, (b) Regression, (c) Artificial Neural Networks, (d) Support Vector Machines and (e) Kernel Ridge Regression, in order to decide which methods and under what configurations are more suitable for that purpose. Following the framework established by the EU COST Action VALUE, in this experiment we test the transferability of these methods to future climate conditions with the use of regional climate models (RCMs) as pseudo observations. We evaluate the marginal aspects of the distributions of daily maximum/minimum temperatures and daily accumulated precipitation, over mainland Spain and the Balearic Islands, analysed by season. For maximum/minimum temperatures all methods display certain transferability issues, being remarkable for Support Vector Machines and Kernel Ridge Regression. For precipitation all methods appear to suffer from transferability difficulties as well, although conclusions are not as clear as for temperature, probably due to the fact that precipitation does not present such a marked signal of change. This study has revealed how an analysis over a historical period is not enough to fully evaluate ESD methods, so we propose that some type of analysis of transferability should be added in a standard procedure of a complete evaluation.

A High-Resolution (20 m) Simulation of Nighttime Low Temperature Inducing Agricultural Crop Damage with the WRF–LES Modeling System

In Korea, sudden cold weather in spring occurs repeatedly every year and causes severe damage to field crops and fruit trees. Detailed forecasting of the daily minimum or suddenly decreasing temperature, closely related to the local topography, has been required in the farmer community. High-resolution temperature models based on empirical formulas or statistical downscaling have fundamental limitations, making it difficult to perform biophysical application and mechanism explanation on small-scale complex terrains. Weather Research and Forecasting–Large Eddy Simulation (WRF–LES) can provide a dynamically and physically scientific tool to be easily applied for farm-scale numerical weather predictions. However, it has been applied mainly for urban areas and in convective boundary layer studies until now. In this study, 20 m resolution WRF–LES simulation of nighttime near-surface temperature and wind was performed for two cold spring weather events that induced significant crop damages in the apple production area and the results were verified with automatic weather station observation data. The study showed that the maximum mean bias of temperature was −1.75 °C and the minimum was −0.68 °C in the spring, while the root mean square error varied between 2.13 and 3.00 °C. The minimum temperature and its duration significantly affected the crop damage, and the WRF–LES could accurately simulate both features. This implies that the application of WRF–LES, with proper nest-domain configuration and harmonized physical options, to the prediction of nighttime frost in rural areas has promising feasibility for orchard- or farm-scale frost prevention and low-temperature management.

Statistical downscaling of daily precipitation over southeastern South America: Assessing the performance in extreme events

AbstractThe performance of multiple empirical statistical downscaling (ESD) methods was assessed for simulating daily precipitation during 1979–2017 over southeastern South America (SESA), a region where extremes are remarkable. Meteorological stations were used as reference and three gridded precipitation products were included to account for observational uncertainty. The set of ESD models involved different configurations of the analogue method (ANs), deterministic and stochastic versions of neural networks (NNs) and generalized linear models (GLMs) and circulation‐conditioned GLMs (GLM_WTs). The years with the largest number of extreme events (wet years) were calibrated separately. The spatio‐temporal variability of extremes was assessed by analysing their intensity, spatial extent, frequency and interannual variability. An overall good performance of the ESD models was found for several aspects of daily precipitation. ESD performance dispersion was usually contained in the observational spread. No particular model configuration was found to perform best in all aspects, indicating the advantage of considering a multi‐model ensemble. The ANs tended to follow the stations, satisfactorily simulating daily precipitation and its extremes. The deterministic GLMs strongly underestimated precipitation estimates and were not able to represent extreme frequencies and intensities, but this was alleviated by employing a stochastic version of the method. Furthermore, the use of weather types to condition the GLMs (GLM_WTs) considerably improved model performance, particularly for the annual cycle and the spatial structure of extreme precipitation. The NNs adequately reproduced the spatial behaviour and intra‐annual variability of extreme precipitation, although they underestimated its intensity in their deterministic version. An analysis of the regional time series of extremes showed consistency among datasets and evidenced the influence of the ENSO teleconnection on the wet years, which were commonly well‐simulated by the statistical models. ESD models presented good skills in simulating a wetter climate over SESA, which is of particular importance in a climate change scenario.

Future snow projections in a small basin of the Western Himalaya

Snow is a crucial component of the hydrological cycle in the Western Himalaya. Water from snowmelt is used in various sectors in downstream regions, thus playing a critical role in securing the livelihoods of millions of people. In this study, we investigated the future evolution of snow cover and snowmelt in the Panjshir catchment of Afghanistan, a sub-basin of the Indus, in the Western Himalaya. We applied a three-step approach to select a few global climate model (GCM) simulations from CMIP5 climate datasets for RCP4.5 and RCP8.5, which showed reasonable performance with ERA5-Land dataset for the chosen historical period (1981–2010). The selected model simulations were then segregated into two groups: those projecting a cold-wet climate and those projecting a warm-dry climate by the end of the 21st century (2071–2100). These GCMs were downscaled to a higher resolution using empirical statistical downscaling. To simulate the snow processes, we used the distributed cryospheric-hydrological J2000 model. The results indicate that the model captures well the snow cover dynamics for the historical period when compared with the daily MODIS-derived snow cover. The J2000 model was then forced by climate projections from the selected GCMs to quantify future changes in snow cover area, snow storage and snowmelt. While a 10–18% reduction in annual snow cover area is projected in the cold-wet models, a 22–36% reduction is projected in the warm-dry models. Similarly, the snow cover area is projected to decrease in all elevation bands under climate change. At the seasonal scale, across all models and scenarios, the snow cover in the autumn and spring seasons are projected to reduce by as much as 25%, with an increase in winter and spring snowmelt and a decrease in summer snowmelt. The projected changes in the seasonal availability of snowmelt-driven water resources are likely to have direct implications for water-dependent sectors in the region and call for a better understanding of water usage and future adaptation practices.

Twenty-first century regional temperature response in Chile based on empirical-statistical downscaling

Local scale estimates of temperature change in the twenty-first century are necessary for informed decision making in both the public and private sector. In order to generate such estimates for Chile, weather station data of the Dirección Meteorológica de Chile are used to identify large-scale predictors for local-scale temperature changes and construct individual empirical-statistical models for each station. The geographical coverage of weather stations ranges from Arica in the North to Punta Arenas in the South. Each model is trained in a cross-validated stepwise linear multiple regression procedure based on (24) weather station records and predictor time series derived from ERA-Interim reanalysis data. The time period 1979–2000 is used for training, while independent data from 2001 to 2015 serves as a basis for assessing model performance. The resulting transfer functions for each station are then directly coupled to MPI-ESM simulations for future climate change under emission scenarios RCP2.6, RCP4.5 and RCP 8.5 to estimate the local temperature response until 2100 A.D. Our investigation into predictors for local scale temperature changes support established knowledge of the main drivers of Chilean climate, i.e. a strong influence of the El Niño Southern Oscillation in northern Chile and frontal system-governed climate in central and southern Chile. Temperature downscaling yields high prediction skill scores (ca. 0.8), with highest scores for the mid-latitudes. When forced with MPI-ESM simulations, the statistical models predict local temperature deviations from the 1979–2015 mean that range between − 0.5–2 K, 0.5–3 K and 2–7 K for RCP2.6, RCP4.5 and RCP8.5 respectively.

Empirical–Statistical Downscaling of Austral Summer Precipitation over South America, with a Focus on the Central Peruvian Andes and the Equatorial Amazon Basin

AbstractPrecipitation is one of the most difficult variables to estimate using large-scale predictors. Over South America (SA), this task is even more challenging, given the complex topography of the Andes. Empirical–statistical downscaling (ESD) models can be used for this purpose, but such models, applicable for all of SA, have not yet been developed. To address this issue, we construct an ESD model using multiple-linear-regression techniques for the period 1982–2016 that is based on large-scale circulation indices representing tropical Pacific Ocean, Atlantic Ocean, and South American climate variability, to estimate austral summer [December–February (DJF)] precipitation over SA. Statistical analyses show that the ESD model can reproduce observed precipitation anomalies over the tropical Andes (Ecuador, Colombia, Peru, and Bolivia), the eastern equatorial Amazon basin, and the central part of the western Argentinian Andes. On a smaller scale, the ESD model also shows good results over the Western Cordillera of the Peruvian Andes. The ESD model reproduces anomalously dry conditions over the eastern equatorial Amazon and the wet conditions over southeastern South America (SESA) during the three extreme El Niños: 1982/83, 1997/98, and 2015/16. However, it overestimates the observed intensities over SESA. For the central Peruvian Andes as a case study, results further show that the ESD model can correctly reproduce DJF precipitation anomalies over the entire Mantaro basin during the three extreme El Niño episodes. Moreover, multiple experiments with varying predictor combinations of the ESD model corroborate the hypothesis that the interaction between the South Atlantic convergence zone and the equatorial Atlantic Ocean provoked the Amazon drought in 2015/16.

A Hybrid Downscaling Approach for Future Temperature and Precipitation Change

AbstractWe used empirical–statistical downscaling in a pseudoreality context, in which both large-scale predictors and small-scale predictands were based on climate model results. The large-scale conditions were taken from a global climate model, and the small-scale conditions were taken from dynamical downscaling of the same global model with a convection-permitting regional climate model covering southern Norway. This hybrid downscaling approach, a “perfect model”–type experiment, provided 120 years of data under the CMIP5 high-emission scenario. Ample calibration samples made rigorous testing possible, enabling us to evaluate the effect of empirical–statistical model configurations and predictor choices and to assess the stationarity of the statistical models by investigating their sensitivity to different calibration intervals. The skill of the statistical models was evaluated in terms of their ability to reproduce the interannual correlation and long-term trends in seasonal 2-m temperature T2m, wet-day frequency fw, and wet-day mean precipitation μ. We found that different 30-yr calibration intervals often resulted in differing statistical models, depending on the specific choice of years. The hybrid downscaling approach allowed us to emulate seasonal mean regional climate model output with a high spatial resolution (0.05° latitude and 0.1° longitude grid) for up to 100 GCM runs while circumventing the issue of short calibration time, and it provides a robust set of empirically downscaled GCM runs.

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

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

Statistical Projection of the North Atlantic Storm Tracks

AbstractA method for empirical–statistical downscaling was adapted to project seasonal cyclone density over the North Atlantic Ocean. To this aim, the seasonal mean cyclone density was derived from instantaneous values of the 6-h mean sea level pressure (SLP) reanalysis fields. The cyclone density was then combined with seasonal mean reanalysis and global climate model projections of SLP or 500-hPa geopotential height to obtain future projections of the North Atlantic storm tracks. The empirical–statistical approach is computationally efficient because it makes use of seasonally aggregated cyclone statistics and allows the future cyclone density to be estimated from the full ensemble of available CMIP5 models rather than from a smaller subset. However, the projected cyclone density in the future differs considerably depending on the choice of predictor, SLP, or 500-hPa geopotential height. This discrepancy suggests that the relationship between the cyclone density and SLP, 500-hPa geopotential height, or both is nonstationary; that is, that the statistical model depends on the calibration period. A stationarity test based on 6-hourly HadGEM2-ES data indicated that the 500-hPa geopotential height was not a robust predictor of cyclone density.

Subsampling Impact on the Climate Change Signal over Poland Based on Simulations from Statistical and Dynamical Downscaling

ABSTRACTMost impact studies using downscaled climate data as input assume that the selection of few global climate models (GCMs) representing the largest spread covers the likely range of future changes. This study shows that including more GCMs can result in a very different behavior. We tested the influence of selecting various subsets of GCMs on the climate change signal over Poland from simulations based on dynamical and empirical–statistical downscaling methods. When the climate variable is well simulated by the GCM, such as temperature, results showed that both downscaling methods agree on a warming over Poland by up to 2° or 5°C assuming intermediate or high emission scenarios, respectively, by 2071–2100. As a less robust simulated signal through GCMs, precipitation is expected to increase by up to 10% by 2071–2100 assuming the intermediate emission scenario. However, these changes are uncertain when the high emission scenario and the end of the twenty-first century are of interest. Further, an additional bootstrap test revealed an underestimation in the warming rate varying from 0.5° to more than 4°C over Poland that was found to be largely influenced by the selection of few driving GCMs instead of considering the full range of possible climate model outlooks. Furthermore, we found that differences between various combinations of small subsets from the GCM ensemble of opportunities can be as large as the climate change signal.

Euro-Cordex Regional Projection Models: What Kind of Agreement for Europe?

EURO-CORDEX is an international initiative which provides regional climate projections based on multiple dynamical and empirical–statistical downscaling models. The goal of this work is to analyse the agreement between projections of the CLMCOM-CCLM4-8-17 (CLMCOM) and SMHI-RCA4 (SMHI) models across Europe. The variables temperature, precipitation and solar radiation were considered for a historical period (1971–2005) and for a future scenario (2006–2040). The overall agreement ($$\mathcal {A}$$) is defined as the normalised area of the magnitude-squared coherence function over the frequency range (averaged over time), being 0 for no agreement and 1 for total agreement between models. The relative mean difference ($$\mathcal {M}$$) and difference between the coefficients of variation ($$\mathcal {V}$$) are also explored, since coherence analysis cannot evaluate differences in mean and variability. Agreement values ranging from 0.32 to 0.74, 0.28 to 0.69 and 0.32 to 0.58 were found for temperature, precipitation and solar radiation, respectively, for the historical period. In all cases, the results show better agreement for lower than higher frequencies. Overall, the time series from both models behave fairly similarly for lower frequencies (i.e. the trend of the time series), while for higher frequencies (i.e. rapid changes in the time series), the similarities between the models are less consistent. For temperature, the $$\mathcal {M}$$ and $$\mathcal {V}$$ values are smaller than 2.5%, while for precipitation and solar radiation they can exceed 50% and 35%, respectively. Further analysis revealed that the contribution of winter and summer differs considerably for $$\mathcal {M}$$ and $$\mathcal {V}$$ values. In conclusion, it seems that such models can provide fairly similar results when considering long periods of time.

Wind Power Potential in Near Future Climate Scenarios: The Case for Burundi (East Africa)

This paper assessed projected changes of wind power potential in near future climate scenarios over four sites from two contrasting regions of Burundi. Observed and MERRA-2 data sets were considered for the historical period 1981-2010, and a computed Multi-model ensemble for future projections data of eight Regional Climate Models under RCP 4.5 and 8.5 over the period 2011-2040 was used. Regional Climate Models were downscaled at local climate using Empirical Statistical Downscaling method. Mann-Kendall’s test was used for trend analysis over the historical period, while future changes in wind power density (WPD) quartiles were computed for each climate scenario by 2040. The findings revealed an increase in wind power potential all over the area studied with higher values during summer time. Indeed, over the period 2011-2040, the lowest WPD change is projected at Northern highlands (NHL) under RCP 4.5 with 27.03 W.m-2, while the highest WPD change of 46.34 W.m-2 is forecasted under RCP 8.5 at Southern Imbo plain (SIP). The month of August and September are expected to have higher WPD change in RCP 4.5 and RCP 8.5, respectively while January is projected to have the lowest WPD. Places near by the Lake Tanganyika are the most favorable areas for wind power generation.

Using statistical downscaling to assess skill of decadal predictions

We test a hypothesis that the signal-to-noise ratio of decadal forecasts improves with larger scales and can be utilised through model output statistics (MOS) involving empirical-statistical downscaling and dependencies to large-scale conditions. Here, we used MOS applied to an ensemble of decadal forecasts to predict local wet-day frequency, the wet-day mean precipitation, and the mean temperature for one to nine year long intervals of forecasts with a one year lead time. Our study involved a set of decadal forecasts over the 1961–2011 period, based on a global coupled ocean-atmosphere model that was downscaled and analysed for the North Atlantic region. The MOS for the decadal forecasts failed to identify aspects that were associated with higher skill for temperature and precipitation over Europe on time scales shorter than five years. A likely explanation for not enhancing skillful parts of the forecasts was that the raw model output had low skill for the general large-scale atmospheric circulation. This was particularly true for the wet-day frequency over Europe, which had a strong connection to the mean sea-level pressure (SLP) anomalies over the North Atlantic. There was a weak connection between large-scale maritime surface temperature anomalies and local precipitation and temperature variability over the European continent. The decadal forecasts for time scale of nine years and longer, on the other hand, exhibited moderate skill. The dependency between temporal and spatial scales was found to differ for the temperature and the mean SLP anomalies, but we found little indication that the decadal predictions for anomalies with large-scale regional extent were associated higher skill than for more local patterns in these forecasts.

Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures

Abstract. A methodology for estimating and downscaling the probability associated with the duration of heatwaves is presented and applied as a case study for Indian wheat crops. These probability estimates make use of empirical-statistical downscaling and statistical modelling of probability of occurrence and streak length statistics, and we present projections based on large multi-model ensembles of global climate models from the Coupled Model Intercomparison Project Phase 5 and three different emissions scenarios: Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5. Our objective was to estimate the probabilities for heatwaves with more than 5 consecutive days with daily maximum temperature above 35 ∘C, which represent a condition that limits wheat yields. Such heatwaves are already quite frequent under current climate conditions, and downscaled estimates of the probability of occurrence in 2010 is in the range of 20 %–84 % depending on the location. For the year 2100, the high-emission scenario RCP8.5 suggests more frequent occurrences, with a probability in the range of 36 %–88 %. Our results also point to increased probabilities for a hot day to turn into a heatwave lasting more than 5 days, from roughly 8 %–20 % at present to 9 %–23 % in 2100 assuming future emissions according to the RCP8.5 scenario; however, these estimates were to a greater extent subject to systematic biases. We also demonstrate a downscaling methodology based on principal component analysis that can produce reasonable results even when the data are sparse with variable quality.