Probabilistic estimates of future changes in California temperature and precipitation usingstatistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling

Efficient dynamical downscaling of general circulation models using continuous data assimilation

Continuous data assimilation (CDA) is successfully implemented for the first time for efficient dynamical downscaling of a global atmospheric reanalysis. A comparison of the performance of CDA with the standard grid and spectral nudging techniques for representing long- and short-scale features in the downscaled fields using the Weather Research and Forecast (WRF) model is further presented and analyzed. The WRF model is configured at 25km horizontal resolution and is driven by 250km initial and boundary conditions from NCEP/NCAR reanalysis fields. Downscaling experiments are performed over a one-month period in January, 2016. The similarity metric is used to evaluate the performance of the downscaling methods for large and small scales. Similarity results are compared for the outputs of the WRF model with different downscaling techniques, NCEP reanalysis, and Final Analysis. Both spectral nudging and CDA describe better the small-scale features compared to grid nudging. The choice of the wave number is critical in spectral nudging; increasing the number of retained frequencies generally produced better small-scale features, but only up to a certain threshold after which its solution gradually became closer to grid nudging. CDA maintains the balance of the large- and small-scale features similar to that of the best simulation achieved by the best spectral nudging configuration, without the need of a spectral decomposition. The different downscaled atmospheric variables, including rainfall distribution, with CDA is most consistent with the observations. The Brier skill score values further indicate that the added value of CDA is distributed over the entire model domain. The overall results clearly suggest that CDA provides an efficient new approach for dynamical downscaling by maintaining better balance between the global model and the downscaled fields

The Key Role of Heavy Precipitation Events in Climate Model Disagreements of Future Annual Precipitation Changes in California

Climate model simulations disagree on whether future precipitation will increase or decrease over California, which has impeded efforts to anticipate and adapt to human-induced climate change. This disagreement is explored in terms of daily precipitation frequency and intensity. It is found that divergent model projections of changes in the incidence of rare heavy (\u3e60 mm day−1) daily precipitation events explain much of the model disagreement on annual time scales, yet represent only 0.3% of precipitating days and 9% of annual precipitation volume. Of the 25 downscaled model projections examined here, 21 agree that precipitation frequency will decrease by the 2060s, with a mean reduction of 6–14 days yr−1. This reduces California\u27s mean annual precipitation by about 5.7%. Partly offsetting this, 16 of the 25 projections agree that daily precipitation intensity will increase, which accounts for a model average 5.3% increase in annual precipitation. Between these conflicting tendencies, 12 projections show drier annual conditions by the 2060s and 13 show wetter. These results are obtained from 16 global general circulation models downscaled with different combinations of dynamical methods [Weather Research and Forecasting (WRF), Regional Spectral Model (RSM), and version 3 of the Regional Climate Model (RegCM3)] and statistical methods [bias correction with spatial disaggregation (BCSD) and bias correction with constructed analogs (BCCA)], although not all downscaling methods were applied to each global model. Model disagreements in the projected change in occurrence of the heaviest precipitation days (\u3e60 mm day−1) account for the majority of disagreement in the projected change in annual precipitation, and occur preferentially over the Sierra Nevada and Northern California. When such events are excluded, nearly twice as many projections show drier future conditions

Harmonized evaluation of daily precipitation downscaled using SDSM and WRF+WRFDA models over the Iberian Peninsula

There have been numerous statistical and dynamical downscaling model comparisons. However, differences in model skill can be distorted by inconsistencies in experimental set-up, inputs and output format. This paper harmonizes such factors when evaluating daily precipitation downscaled over the Iberian Peninsula by the Statistical DownScaling Model (SDSM) and two configurations of the dynamical Weather Research and Forecasting Model (WRF) (one with data assimilation (D) and one without (N)). The ERA-Interim reanalysis at 0.75◦ resolution provides common inputs for spinning-up and driving the WRF model and calibrating SDSM. WRF runs and SDSM output were evaluated against ECA&D stations, TRMM, GPCP and EOBS gridded precipitation for 2010–2014 using the same suite of diagnostics. Differences between WRF and SDSM are comparable to observational uncertainty, but the relative skill of the downscaling techniques varies with diagnostic. The SDSM ensemble mean, WRF-D and ERAI have similar correlation scores (r = 0.45–0.7), but there were large variations amongst SDSM ensemble members (r = 0.3–0.6). The best Linear Error in Probability Space (LEPS = 0.001–0.007) and simulations of precipitation amount were achieved by individual members of the SDSM ensemble. However, the Brier Skill Score shows these members do not improve the prediction by ERA-Interim, whereas precipitation occurrence is reproduced best by WRF-D. Similar skill was achieved by SDSM when applied to station or gridded precipitation data. Given the greater computational demands of WRF compared with SDSM, clear statements of expected value-added are needed when applying the former to climate impacts and adaptation research

Climate across scales: the downscaling of precipitation for a basin in atropical mountain region in the Andes of Southern Ecuador

The main objective of this research is to study the downscaling of precipitation at basin scale in the Paute river basin, which is located in the tropical Andes of Southern Ecuador. The main assumption is that, by incorporating orographic information in the downscaling of precipitation, improved estimates of precipitation can be achieved. Such research is important, both from a scientific perspective as well as for water resource management and planning assessment for this developing country

Statistical downscaling of air temperature in the Douro Valley for agronomic applications

Tese de mestrado em Ciências Geofísicas (Meteorologia), apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2013Agronomic activities are very dependent on local climatic conditions. The vineyard in particular is very sensitive to temperature, which significantly affects the composition of grapes and hence the final quality of the produced wine. In a climate change context knowledge of future temperature variability is important to minimize impacts and promote adaptation measures often entailing high costs. However, given the local character of agronomic activities, temperature projections are required at very small spatial scales, and downscaling of climate variables is therefore required. In this thesis temperature data from the high resolution (9km) meteorological model WRF and reanalysis data from ERA-interim are analyzed. Statistical downscaling techniques are applied to the ERA-interim data in order to obtain local temperature estimates for the wine producing region of the Douro valley. Several bioclimatic indices based on downscaled temperature are further calculated in order to evaluate the climatic potential of the Douro Wine Region.No contexto das alterações climáticas os impactos da variabilidade da temperatura têm sido um dos principais objectos de estudo ao longo do último século. A prática vitícola, em particular, é uma das actividades agronómicas mais influenciadas pela temperatura, e a sua importância económica para Portugal conduziu a vários estudos sobre este tópico. A Região Vinhateira do Douro constitui um excelente exemplo da contribuição dos produtores de vinho para o crescimento económico, e de como a complexa topografia da região contribui para a variabilidade climática, muitas vezes com consequências directas na qualidade final do vinho. Esta tese contribui para o conhecimento das condições climáticas locais da Região Vinhateira do Douro que influenciam a composição das uvas e a consequente qualidade do vinho produzido. O impacto das alterações climáticas na qualidade do vinho da Região Vinhateira do Douro usando GCMs (General Circulation Models também conhecidos como Global Climate Models) e RCMs (Regional Climate Models) é discutido por vários autores. Contudo, a baixa resolução das grelhas dos GCMs, dos RCMs e da reanálise negligenciam aspectos regionais, e técnicas que permitam a obtenção de informação de menor escala surgem como um requisito essencial nas ciências agronómicas. A Região Vinhateira do Douro em particular é um excelente exemplo da necessidade de climatologia de alta resolução, motivada pela geomorfologia complexa da região. O objectivo deste trabalho é a realização de um downscaling estatístico da temperatura do ar para locais particulares de modo a focar em áreas localizadas da Região Vinhateira do Douro, com a intenção de poder ser aplicado no estudo de uma vinha em particular. Existem vários métodos de downscaling com o propósito de colmatar o problema de baixa resolução dos GCMs e RCMs, que são geralmente subdivididos em duas categorias: downscaling dinâmico e estatístico. O downscaling dinâmico é uma abordagem numérica que consiste na utilização de modelos globais ou reanálise como forçadores de modo a obter simulações de dados mais detalhadas para uma região particular. O downscaling estatístico utiliza modelos estatísticos simples, de modo a estabelecer a relação estatística entre variáveis de grande escala e variáveis locais. Os modelos de regressão são bastante utilizados para downscaling estatístico destacando-se pelo seu custo computacional reduzido e a sua fácil aplicação. Neste trabalho são consideradas três estações meteorológicas na Região Vinhateira do Douro, Vila Real, Pinhão e Régua, representando duas das três sub-regiões da Região Demarcada do Douro: Baixo Corgo (Régua e Vila Real) e Cima Corgo (Pinhão). Baixo Corgo é a sub-região que apresenta as temperaturas mais baixas devido à influência dos ventos do Atlântico, sendo protegida pelas serras do Marão e Montemuro, enquanto Cima Corgo apresenta temperaturas mais elevadas. Em contraste, a sub-região mais a este, Douro Superior, é a sub-região mais quente e mais seca e que tem as plantações de vinhas mais recentes, marcada por episódios de seca recorrentes. As estações meteorológicas em análise são também representativas das características topográficas que contribuem para o clima único da região, com altitudes de 481, 65 e 130 metros respectivamente. A mais recente reanálise do ECMWF (European Centre for Medium Range Forecasts), ERA-Interim, e um RCM estado-de-arte resultante de um downscaling dinâmico, WRF (9km) são utilizados para a realização do downscaling estatístico da temperatura do ar para a localização das estações. A suave topografia da reanálise ERA-Interim e do modelo WRF são ajustadas através de um gradiente de temperatura constante de 6ºC/km. O downscaling estatístico realizado neste trabalho é baseado em métodos de regressão. Como pré-processamento na análise dos dados de temperatura é aplicada uma decomposição das séries temporais utilizando o método STL (Seasonal-Trend decomposition procedure based on Loess), um algoritmo iterativo e robusto baseado em regressão local. O ajuste sazonal das séries temporais é um passo fulcral para a análise de regressão e, neste trabalho, é obtido pela remoção da componente sazonal obtida pelo método STL. Neste trabalho, a técnica de regressão baseada em mínimos quadrados ordinários é primeiro considerada, e posteriormente o método de regressão robusta é aplicado de modo a reduzir o impacto de eventuais outliers nos resultados. A relação estatística entre a reanálise/WRF e as observações é estabelecida a partir das séries temporais ajustadas sazonalmente para o período de calibração de 1989-2003. O downscaling estatístico da reanálise ERA-Interim e a combinação de downscaling dinâmico e estatístico do modelo WRF é realizado no período de validação de 2004-2006. O correspondente ciclo sazonal da reanálise ERA-Interim e do modelo WRF são adicionados posteriormente às séries temporais downscaled, dado que o ciclo sazonal médio é semelhante ao das observações. O ciclo sazonal das observações não é considerado neste trabalho dado que não seria possível a sua utilização no caso da aplicação desta técnica de downscaling para linhas temporais no futuro. De modo a avaliar o downscaling estatístico, quatro medidas de precisão estatística são utilizadas: o viés, a raiz do erro médio quadrático, o erro absoluto médio e o erro percentual absoluto. Como etapa final, as séries locais de temperatura obtidas por downscaling estatístico são utilizadas para avaliar o potencial climático para crescimento da uva, nas estações em estudo da Região Vinhateira do Douro. A caracterização do clima nesta região é realizada a partir de índices bioclimáticos baseados na temperatura durante o período de crescimento das videiras (Abril a Outubro). A temperatura média do período de crescimento (GST, Average growing season temperature) é calculada a partir da soma da média da temperatura média, durante os sete meses do período de crescimento. O índice GDD (Growing degree-days) corresponde à temperatura média acima de uma temperatura base de 10ºC, uma vez que não existe crescimento da uva abaixo desta temperatura, e permite descrever o tempo envolvido nos processos biológicos da videira. Semelhante a este último é o índice helio-térmico de Huglin (HI, Heliothermal Index of Huglin) que dá mais peso à temperatura máxima e considera um coeficiente de ajustamento devido à variação em latitude. A duração do período de crescimento é dada pelo LGS (Length growing season) que considera o número de dias em quem a temperatura média está acima dos 10ºC. O CI (Cool Nigth Index) é complementar ao HI e tem conta a média da temperatura mínima durante o período de maturação (Setembro). De acordo com os valores de cada índice é possível definir classes climáticas características do potencial climático de cada região. Um dos principais resultados deste trabalho reside na excelente representação da variabilidade da temperatura máxima, mínima e média pelas séries temporais downscaled estatisticamente. De um modo geral, a regressão baseada em mínimos quadrados ordinários e a regressão robusta apresentam resultados semelhantes, indicando que o impacto de eventuais outliers não é significativo na variabilidade média. Verifica-se que o downscaling estatístico reduz significativamente as diferenças entre a ERA-Interim/WRF e as observações, revelando a importância do downscaling estatístico em aumentar a performance da reanálise ERA-Interim e do modelo WRF, e o valor adicional em combinar downscaling dinâmico e estatístico. Os índices bioclimáticos calculados a partir das séries downscaled estatisticamente destacam-se como sendo uma excelente aproximação dos índices calculados a partir das observações e constituem uma melhoria significativa do que se obteria a partir apenas da reanálise ERA-Interim e do modelo WRF. No que diz respeito à aplicação na vinha, o downscaling estatístico revela ser uma mais-valia ao capturar características locais, tal como a influência da altura das estações

Reference evapotranspiration from coarse-scale and dynamically downscaled data in complex terrain: Sensitivity to interpolation and resolution

The main objective of this study was to investigate whether dynamically downscaled high resolution (4-km) climate data from the Weather Research and Forecasting (WRF) model provide physically meaningful additional information for reference evapotranspiration (E) calculation compared to the recently published GridET framework that uses interpolation from coarser-scale simulations run at 32-km resolution. The analysis focuses on complex terrain of Utah in the western United States for years 1985–2010, and comparisons were made statewide with supplemental analyses specifically for regions with irrigated agriculture. E was calculated using the standardized equation and procedures proposed by the American Society of Civil Engineers from hourly data, and climate inputs from WRF and GridET were debiased relative to the same set of observations. For annual mean values, E from WRF (EW) and E from GridET (EG) both agreed well with E derived from observations (r2 = 0.95, bias \u3c 2 mm). Domain-wide, EW and EG were well correlated spatially (r2 = 0.89), however local differences ΔE=EW-EG were as large as +439 mm year−1 (+26%) in some locations, and ΔE averaged +36 mm year−1. After linearly removing the effects of contrasts in solar radiation and wind speed, which are characteristically less reliable under downscaling in complex terrain, approximately half the residual variance was accounted for by contrasts in temperature and humidity between GridET and WRF. These contrasts stemmed from GridET interpolating using an assumed lapse rate of Γ = 6.5K km−1, whereas WRF produced a thermodynamically-driven lapse rate closer to 5K km−1 as observed in mountainous terrain. The primary conclusions are that observed lapse rates in complex terrain differ markedly from the commonly assumed Γ = 6.5K km−1, these lapse rates can be realistically resolved via dynamical downscaling, and use of constant Γ produces differences in E of order as large as 102 mm year−1

Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission