Regional climate of hazardous convective weather through high-resolution dynamical downscaling

We explore the use of high-resolution dynamical downscaling as a means to simulate the regional climatology and variability of hazardous convective-scale weather. Our basic approach differs from a traditional regional climate model application in that it involves a sequence of daily integrations. We use the weather research and forecasting (WRF) model, with global reanalysis data as initial and boundary conditions. Horizontal grid lengths of 4.25?km allow for explicit representation of deep convective storms and hence a compilation of their occurrence statistics over a large portion of the conterminous United States. The resultant 10-year sequence of WRF model integrations yields precipitation that, despite its positive bias, has a diurnal cycle consistent with observations, and otherwise has a realistic geographical distribution. Similarly, the occurrence frequency of short-duration, potentially flooding rainfall compares well to analyses of hourly rain gauge data. Finally, the climatological distribution of hazardous-thunderstorm occurrence is shown to be represented with some degree of skill through a model proxy that relates rotating convective updraft cores to the presence of hail, damaging surface winds, and tornadoes. The results suggest that the proxy occurrences, when coupled with information on the larger-scale atmosphere, could provide guidance on the reliability of trends in the observed occurrences.     

A high-resolution ocean-atmosphere coupled downscaling of the present climate over California

A fully coupled regional ocean-atmosphere model system that consists of the regional spectral model and the regional ocean modeling system for atmosphere and ocean components, respectively, is applied to downscale the present climate (1985–1994) over California from a global simulation of the Community Climate System Model 3.0 (CCSM3). The horizontal resolution of the regional coupled modeling system is 10 km, while that of the CCSM3 is at a spectral truncation of T85 (approximately 1.4°). The effects of the coupling along the California coast in the boreal summer and winter are highlighted. Evaluation of the sea surface temperature (SST) and 2-m air temperature climatology shows that alleviation of the warm bias along the California coast in the global model output is clear in the regional coupled model run. The 10-m wind is also improved by reducing the northwesterly winds along the coast. The higher resolution coupling effect on the temperature and specific humidity is the largest near the surface, while the significant impact on the wind magnitude appears at a height of approximately 850-hPa heights. The frequency of the Catalina Eddy and its duration are increased by more than 60 % in the coupled downscaling, which is attributed to enhanced offshore sea-breeze. Our study indicates that coupling is vital to regional climate downscaling of mesoscale phenomena over coastal areas.     

Realised added value in dynamical downscaling of Australian climate change

Climate Dynamics - Coarse resolution global climate models (GCMs) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional...     

Evaluation of a WRF dynamical downscaling simulation over California

This paper presents results from a 40 year Weather Research and Forecasting (WRF) based dynamical downscaling experiment performed at 12 km horizontal grid spacing, centered on the state of California, and forced by a 1° × 1.25° finite-volume current-climate Community Climate System Model ver. 3 (CCSM3) simulation. In-depth comparisons between modeled and observed regional-average precipitation, 2 m temperature, and snowpack are performed. The regional model reproduces the spatial distribution of precipitation quite well, but substantially overestimates rainfall along windward slopes. This is due to strong overprediction of precipitation intensity; precipitation frequency is actually underpredicted by the model. Moisture fluxes impinging on the coast seem to be well-represented over California, implying that precipitation bias is caused by processes internal to WRF. Positive-definite moisture advection and use of the Grell cumulus parameterization result in some decrease in precipitation bias, but other sources are needed to explain the full bias magnitude. Surface temperature is well simulated in all seasons except summer, when overly-dry soil moisture results in a several degree warm bias in both CCSM3 and WRF. Additionally, coastal temperatures appear to be too warm due to a coastal sea surface temperature bias inherited from CCSM3. Modeled snowfall/snowmelt agrees quite well with observations, but snow water equivalent is found to be much too low due to monthly reinitialization of all regional model fields from CCSM3 values.     

Estimation methods for monthly humidity from dynamical downscaling data for quantitative assessments of climate change impacts

Methods are proposed to estimate the monthly relative humidity and wet bulb temperature based on observations from a dynamical downscaling coupled general circulation model with a regional climate model (RCM) for a quantitative assessment of climate change impacts. The water vapor pressure estimation model developed was a regression model with a monthly saturated water vapor pressure that used minimum air temperature as a variable. The monthly minimum air temperature correction model for RCM bias was developed by stepwise multiple regression analysis using the difference in monthly minimum air temperatures between observations and RCM output as a dependent variable and geographic factors as independent variables. The wet bulb temperature was estimated using the estimated water vapor pressure, air temperature, and atmospheric pressure at ground level both corrected for RCM bias. Root mean square errors of the data decreased considerably in August.     

A regional climate model downscaling projection of China future climate change

Climate changes over China from the present (1990–1999) to future (2046–2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 downscaling corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 downscaling produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility.     

Comparison of a very-fine-resolution GCM with RCM dynamical downscaling in simulating climate in China

Regional climate simulation can generally be improved by using an RCM nested within a coarser-resolution GCM. However, whether or not it can also be improved by the direct use of a state-of-the-art GCM with very fine resolution, close to that of an RCM, and, if so, which is the better approach, are open questions. These questions are important for understanding and using these two kinds of simulation approaches, but have not yet been investigated. Accordingly, the present reported work compared simulation results over China from a very-fine-resolution GCM (VFRGCM) and from RCM dynamical downscaling. The results showed that: (1) The VFRGCM reproduces the climatologies and trends of both air temperature and precipitation, as well as inter-monthly variations of air temperature in terms of spatial pattern and amount, closer to observations than the coarse-resolution version of the GCM. This is not the case, however, for the inter-monthly variations of precipitation. (2) The VFRGCM captures the climatology, trend, and inter-monthly variation of air temperature, as well as the trend in precipitation, more reasonably than the RCM dynamical downscaling method. (3) The RCM dynamical downscaling method performs better than the VFRGCM in terms of the climatology and inter-monthly variation of precipitation. Overall, the results suggest that VFRGCMs possess great potential with regard to their application in climate simulation in the future, and the RCM dynamical downscaling method is still dominant in terms of regional precipitation simulation.     

Future climate change scenarios over Korea using a multi-nested downscaling system: A pilot study

This study examines a scenario of future summer climate change for the Korean peninsula using a multi-nested regional climate system. The global-scale scenario from the ECHAM5, which has a 200 km grid, was downscaled to a 50 km grid over Asia using the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM). This allowed us to obtain large-scale forcing information for a one-way, double-nested Weather and Research Forecasting (WRF) model that consists of a 12 km grid over Korea and a 3 km grid near Seoul. As a pilot study prior to the multi-year simulation work the years 1995 and 2055 were selected for the present and future summers. This RSM-WRF multi-nested downscaling system was evaluated by examining a downscaled climatology in 1995 with the largescale forcing from the NCEP/Department of Energy (DOE) reanalysis. The changes in monsoonal flows over East Asia and the associated precipitation change scenario over Korea are highlighted. It is found that the RSM-WRF system is capable of reproducing large-scale features associated with the East-Asian summer monsoon (EASM) and its associated hydro-climate when it is nested by the NCEP/DOE reanalysis. The ECHAM5-based downscaled climate for the present (1995) summer is found to suffer from a weakening of the low-level jet and sub-tropical high when compared the reanalysis-based climate. Predicted changes in summer monsoon circulations between 1995 and 2055 include a strengthened subtropical high and an intensified mid-level trough. The resulting projected summer precipitation is doubled over much of South Korea, accompanied by a pronounced surface warming with a maximum of about 2 K. It is suggested that downscaling strategy of this study, with its cloud-resolving scale, makes it suitable for providing high-resolution meteorological data with which to derive hydrology or air pollution models.     

Evaluating climate change effects on runoff by statistical downscaling and hydrological model GR2M

The main purpose of this study is to evaluate the impacts of climate change on Izmir-Tahtali freshwater basin, which is located in the Aegean Region of Turkey. For this purpose, a developed strategy involving statistical downscaling and hydrological modeling is illustrated through its application to the basin. Prior to statistical downscaling of precipitation and temperature, the explanatory variables are obtained from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data set. All possible regression approach is used to establish the most parsimonious relationship between precipitation, temperature, and climatic variables. Selected predictors have been used in training of artificial neural networks-based downscaling models and the trained models with the obtained relationships have been operated to produce scenario precipitation and temperature from the simulations of third Generation Coupled Climate Model. Biases from downscaled outputs have been reduced after downscaling process. Finally, the corrected downscaled outputs have been transformed to runoff by means of a monthly parametric hydrological model GR2M to assess the probable impacts of temperature and precipitation changes on runoff. According to the A1B climate scenario results, statistically significant trends are foreseen for precipitation, temperature, and runoff in the study basin.     

A cautionary note on automated statistical downscaling methods for climate change

The urge for higher resolution climate change scenarios has been widely recognized, particularly for conducting impact assessment studies. Statistical downscaling methods have shown to be very convenient for this task, mainly because of their lower computational requirements in comparison with nested limited-area regional models or very high resolution Atmosphere–ocean General Circulation Models. Nevertheless, although some of the limitations of statistical downscaling methods are widely known and have been discussed in the literature, in this paper it is argued that the current approach for statistical downscaling does not guard against misspecified statistical models and that the occurrence of spurious results is likely if the assumptions of the underlying probabilistic model are not satisfied. In this case, the physics included in climate change scenarios obtained by general circulation models, could be replaced by spatial patterns and magnitudes produced by statistically inadequate models. Illustrative examples are provided for monthly temperature for a region encompassing Mexico and part of the United States. It is found that the assumptions of the probabilistic models do not hold for about 70 % of the gridpoints, parameter instability and temporal dependence being the most common problems. As our examples reveal, automated statistical downscaling “black-box” models are to be considered as highly prone to produce misleading results. It is shown that the Probabilistic Reduction approach can be incorporated as a complete and internally consistent framework for securing the statistical adequacy of the downscaling models and for guiding the respecification process, in a way that prevents the lack of empirical validity that affects current methods.     

Probabilistic estimates of future changes in California temperature and precipitation using statistical 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.     

Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal

Realizing the error characteristics of regional climate models (RCMs) and the consequent limitations in their direct utilization in climate change impact research, this study analyzes a quantile-based empirical-statistical error correction method (quantile mapping, QM) for RCMs in the context of climate change. In particular the success of QM in mitigating systematic RCM errors, its ability to generate “new extremes” (values outside the calibration range), and its impact on the climate change signal (CCS) are investigated. In a cross-validation framework based on a RCM control simulation over Europe, QM reduces the bias of daily mean, minimum, and maximum temperature, precipitation amount, and derived indices of extremes by about one order of magnitude and strongly improves the shapes of the related frequency distributions. In addition, a simple extrapolation of the error correction function enables QM to reproduce “new extremes” without deterioration and mostly with improvement of the original RCM quality. QM only moderately modifies the CCS of the corrected parameters. The changes are related to trends in the scenarios and magnitude-dependent error characteristics. Additionally, QM has a large impact on CCSs of non-linearly derived indices of extremes, such as threshold indices.     

Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California, under two emissions scenarios

A hydrologic model was driven by the climate projected by 11 GCMs under two emissions scenarios (the higher emission SRES A2 and the lower emission SRES B1) to investigate whether the projected hydrologic changes by 2071–2100 have a high statistical confidence, and to determine the confidence level that the A2 and B1 emissions scenarios produce differing impacts. There are highly significant average temperature increases by 2071–2100 of 3.7°C under A2 and 2.4°C under B1; July increases are 5°C for A2 and 3°C for B1. Two high confidence hydrologic impacts are increasing winter streamflow and decreasing late spring and summer flow. Less snow at the end of winter is a confident projection, as is earlier arrival of the annual flow volume, which has important implications on California water management. The two emissions pathways show some differing impacts with high confidence: the degree of warming expected, the amount of decline in summer low flows, the shift to earlier streamflow timing, and the decline in end-of-winter snow pack, with more extreme impacts under higher emissions in all cases. This indicates that future emissions scenarios play a significant role in the degree of impacts to water resources in California.     

Optimal selection of representative climate models and statistical downscaling for climate change impact studies: a case study of Rhode Island,USA

Theoretical and Applied Climatology - The future climate impact studies rely on future projections obtained from downscaling of Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models....     

分位数调整法在北京动力降尺度模拟订正中的适用性评估

基于分位数调整法对变网格模式LMDZ4在中国区域进行动力降尺度模拟的北京日平均气温和降水结果进行了统计误差订正。订正后的日平均气温在年循环、平均值和频率等方面均十分接近观测值,全年平均气温偏差由-1.2℃降至-0.4℃。降水的订正过程较气温更加复杂,首先对降水日数进行订正,以消除模式产生的虚假微量降水,订正后降水日数误差由61.5%降至3.7%。此外,分位数调整法可有效订正中小型与极端降水的频率和强度,订正后全年降水误差由0.28 mm/d降至0.07 mm/d。订正后最大降水月为7月,与观测一致,消除了冬季的虚假极端降水。分位数调整法无论是对气温还是降水,其订正效果都存在明显的季节性差异。日平均气温的订正在冬、夏季要优于春、秋季,对极端高、低气温的订正更加显著。该统计误差订正方法不仅有效消除了气候平均值的漂移,同时对极值也有一定改善,是一种相对完善的订正方案。分位数调整法也存在一定的不确定性,订正效果受观测资料和模式模拟能力影响较大。     

Prediction of climate change in Brunei Darussalam using statistical downscaling model

Theoretical and Applied Climatology - Climate is changing and evidence suggests that the impact of climate change would influence our everyday lives, including agriculture, built environment,...     

1,800 Years of abrupt climate change, severe fire, and accelerated erosion, Sierra Nevada, California, USA

This paper provides both a detailed history of environmental change in the Sierra Nevada over the past 1,800 years and evidence for climate teleconnections between the Sierra Nevada and Greenland during the late Holocene. A review of Greenland ice core data suggests that the magnitudes of abrupt changes in temperature and precipitation increased beginning c. 3,700 and 3,000 years ago, respectively. Precipitation increased abruptly 1,300 years ago. Comparing paleotemperature data from Cirque Peak, CA with paleoprecipitation data from Pyramid Lake, NV suggests that hot temperatures occurred at the beginnings of most severe droughts in the Sierra Nevada over the past 1,800 years. Severe fires and erosion also occurred at Coburn Lake, CA at the beginning of all severe droughts in the Sierra Nevada over the past 1,800 years. This suggests that abrupt climate change during the late Holocene caused vegetation and mountain slopes in some areas to be out of equilibrium with abruptly changed climates. Finally, the ending of drought conditions in Greenland coincided with the beginning of drought conditions in the Sierra Nevada over the past 1,800 years, perhaps as a result of the rapidly changed locations of the Earth??s major precipitation belts during abrupt climate change events.     

Rancher and farmer perceptions of climate change in Nevada, USA

Farming and ranching communities in arid lands are vulnerable to the adverse impacts of climate change. We surveyed Nevada ranchers and farmers (n?=?481) during 2009–2010 to assess climate change related knowledge, assumptions, and perceptions. The large majority of this group agreed that we are in a period of climate change; however, only 29 % of them believed that human activity is playing a significant role. Female ranchers and farmers hold more scientifically accurate knowledge about climate change than do their male counterparts, regardless of Democratic or Republican affiliation. Partisan affiliation, political ideology, and gender have strong impacts on climate change knowledge and perceptions. Republican, conservative and male rural residents view climate change as a low national priority, less important to themselves, and less harmful to their communities. Female ranchers and farmers are more concerned about the negative impacts of climate change. We found that only 4 % of our subjects (n?=?299) attribute local environment changes to climate change or global warming. The knowledge gained from this study will help researchers and natural resource managers understand how to best communicate about climate change with rural communities, and support policy makers in identifying potentially effective adaptation and mitigation policies and outreach programs.