A comparison of explicit and implicit spatial downscaling of GCM output for soil erosion and crop production assessments

Spatial downscaling of climate change scenarios can be a significant source of uncertainty in simulating climatic impacts on soil erosion, hydrology, and crop production. The objective of this study is to compare responses of simulated soil erosion, surface hydrology, and wheat and maize yields to two (implicit and explicit) spatial downscaling methods used to downscale the A2a, B2a, and GGa1 climate change scenarios projected by the Hadley Centre’s global climate model (HadCM3). The explicit method, in contrast to the implicit method, explicitly considers spatial differences of climate scenarios and variability during downscaling. Monthly projections of precipitation and temperature during 1950–2039 were used in the implicit and explicit spatial downscaling. A stochastic weather generator (CLIGEN) was then used to disaggregate monthly values to daily weather series following the spatial downscaling. The Water Erosion Prediction Project (WEPP) model was run for a wheat–wheat–maize rotation under conventional tillage at the 8.7 and 17.6% slopes in southern Loess Plateau of China. Both explicit and implicit methods projected general increases in annual precipitation and temperature during 2010–2039 at the Changwu station. However, relative climate changes downscaled by the explicit method, as compared to the implicit method, appeared more dynamic or variable. Consequently, the responses to climate change, simulated with the explicit method, seemed more dynamic and sensitive. For a 1% increase in precipitation, percent increases in average annual runoff (soil loss) were 3–6 (4–10) times greater with the explicit method than those with the implicit method. Differences in grain yield were also found between the two methods. These contrasting results between the two methods indicate that spatial downscaling of climate change scenarios can be a significant source of uncertainty, and further underscore the importance of proper spatial treatments of climate change scenarios, and especially climate variability, prior to impact simulation. The implicit method, which applies aggregated climate changes at the GCM grid scale directly to a target station, is more appropriate for simulating a first-order regional response of nature resources to climate change. But for the site-specific impact assessments, especially for entities that are heavily influenced by local conditions such as soil loss and crop yield, the explicit method must be used.     

Climate Change Estimates of Summer Temperature and Precipitation in the Alpine Region

Summary Climatic changes of summer temperature and precipitation in the greater Alpine region are assessed by using statistical-dynamical downscaling. The downscaling procedure is applied to two 30-year periods (1971–2000 and 2071–2100, summer months only) taken from the results of a transient coupled ocean/atmosphere climate scenario simulation with increasing greenhouse gas concentrations. The downscaling results for the present-day climate are compared with observations. The estimated regional climate change during the next 100 years shows a general warming. The mean summer temperatures increase by 3 to 5 Kelvin. The most intense climatic warming is predicted in the western parts of the Alps. The amount of summer precipitation decreases in most parts of central Europe by more than 20 percent. Increasing precipitation is simulated only over the Adriatic area and parts of eastern central Europe. The results are compared with observed climate trends for the last decades and results of other regional climate change estimations. The observed trends and the majority of the simulated trends (including ours) have a number of common features. However, there are also climate change estimates of other groups which completely contradict our results. Received April 8, 1999 Revised November 16, 1999     

Statistical downscaling of sea-surface wind over the Peru�CChile upwelling region: diagnosing the impact of climate change from the IPSL-CM4 model

The key aspect of the ocean circulation off Peru?CChile is the wind-driven upwelling of deep, cold, nutrient-rich waters that promote a rich marine ecosystem. It has been suggested that global warming may be associated with an intensification of upwelling-favorable winds. However, the lack of high-resolution long-term observations has been a limitation for a quantitative analysis of this process. In this study, we use a statistical downscaling method to assess the regional impact of climate change on the sea-surface wind over the Peru?CChile upwelling region as simulated by the global coupled general circulation model IPSL-CM4. Taking advantage of the high-resolution QuikSCAT wind product and of the NCEP reanalysis data, a statistical model based on multiple linear regressions is built for the daily mean meridional and zonal wind at 10?m for the period 2000?C2008. The large-scale 10?m wind components and sea level pressure are used as regional circulation predictors. The skill of the downscaling method is assessed by comparing with the surface wind derived from the ERS satellite measurements, with in situ wind observations collected by ICOADS and through cross-validation. It is then applied to the outputs of the IPSL-CM4 model over stabilized periods of the pre-industrial, 2?×?CO₂ and 4?×?CO₂ IPCC climate scenarios. The results indicate that surface along-shore winds off central Chile (off central Peru) experience a significant intensification (weakening) during Austral winter (summer) in warmer climates. This is associated with a general decrease in intra-seasonal variability.     

Comparison of Atmospheric Circulation in the Area of Spitsbergen in 1920–1950 and in the Modern Warming Period

The results of studying the temporal variability of atmospheric circulation in the Western Arctic (the Norwegian and Barents seas) are presented. The daily dataset of Girs-Vangengeim E, W, and C circulation forms for the period of 1891–2016 is used to describe atmospheric circulation. Special attention is given to the estimation of differences in weather conditions during the modern period of warming (1985–2015) and in the period of the first Arctic warming (1920–1950). For the cold (November-March) and warm (April-October) seasons, the trends in the frequency of occurrence of the circulation forms are determined. The occurrence of the number of consecutive days with the same atmospheric circulation form which can be considered as a characteristic of weather stability during the analyzed period of warming, is computed for both seasons. The prevalence of the E circulation form during the warm season is typical of both periods. The modern period of warming in the study area, as compared to the period of the first warming, is characterized by an increase in the occurrence of the C circulation form with a short duration. It is found that the current climate regime is characterized by an increase in surface air temperature against a background of less stable weather conditions.     

Changing growing season observed in Canada

It is theoretically interesting for climate change detection and practically important for agricultural producers to know whether climate change has influenced agroclimatic conditions and, if so, what the potential impacts are. We present analyses on statistical differences in means and variances of agroclimatic indices between three 30-year periods in the 20th century (i.e., 1911–1940, 1941–1970 and 1971–2000). We found many occurrences of statistically significant changes in means between pairs of the three 30-year periods. The findings consistently support agroclimatic trends identified from trend analysis as an earlier growing season start and an earlier end to spring frost (SF), together with an extended growing season, more frost-free days (FFD) and more available heat units were often found in the later 30-year periods as compared to the earlier ones. In addition, this study provides more detailed quantitative information than the trend signals for the practical interests of agricultural applications. Significant changes were detected for SF and FFD at a much larger percentage of stations between the latter two 30-year periods (1941–1970 vs. 1971–2000) as compared to the earlier two periods (1911–1940 vs. 1941–1970). In contrast, changes in variances of the selected agroclimatic indices were less evident than changes in their means, based on the percentage of stations showing significant differences. We also present new climate averages of the selected agroclimatic indices that can be useful for agricultural planning and management.     

Past,present and future vegetation-cloud feedbacks in the Amazon Basin

To begin exploring the underlying mechanisms that couple vegetation to cloud formation processes, we derive the lifting condensation level (LCL) to estimate cumulus cloud base height. Using a fully coupled land–ocean–atmosphere general circulation model (HadCM3LC), we investigate Amazonian forest feedbacks on cloud formation over three geological periods; modern-day (a.d. 1970–1990), the last glacial maximum (LGM; 21 kya), and under a future climate scenario (IS92a; a.d. 2070–2090). Results indicate that for both past and future climate scenarios, LCL is higher relative to modern-day. Statistical analyses indicate that the 800 m increase in LCL during the LGM is related primarily to the drier atmosphere promoted by lower tropical sea surface temperatures. In contrast, the predicted 1,000 m increase in LCL in the future scenario is the result of a large increase in surface temperature and reduced vegetation cover.     

Potential climate change impact on wind energy resources in northern Europe: analyses using a regional climate model

There is considerable interest in the potential impact of climate change on the feasibility and predictability of renewable energy sources including wind energy. This paper presents dynamically downscaled near-surface wind fields and examines the impact of climate change on near-surface flow and hence wind energy density across northern Europe. It is shown that: Simulated wind fields from the Rossby Centre coupled Regional Climate Model (RCM) (RCAO) with boundary conditions derived from ECHAM4/OPYC3 AOGCM and the HadAM3H atmosphere-only GCM exhibit reasonable and realistic features as documented in reanalysis data products during the control period (1961–1990). The near-surface wind speeds calculated for a climate change projection period of 2071–2100 are higher than during the control run for two IPCC emission scenarios (A2, B2) for simulations conducted using boundary conditions from ECHAM4/OPYC3. The RCAO simulations conducted using boundary conditions from ECHAM4/OPYC3 indicate evidence for a small increase in the annual wind energy resource over northern Europe between the control run and climate change projection period and for more substantial increases in energy density during the winter season. However, the differences between the RCAO simulations for the climate projection period and the control run are of similar magnitude to differences between the RCAO fields in the control period and the NCEP/NCAR reanalysis data. Additionally, the simulations show a high degree of sensitivity to the boundary conditions, and simulations conducted using boundary conditions from HadAM3H exhibit evidence of slight declines or no change in wind speed and energy density between 1961–1990 and 2071–2100. Hence, the uncertainty of the projected wind changes is relatively high.     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.     

An Improved Statistical-Dynamical Downscaling Scheme and its Application to the Alpine Precipitation Climatology

Summary An improved statistical-dynamical downscaling method for the regionalization of large-scale climate analyses or simulations is introduced. The method is based on the disaggregation of a multi-year time-series of large-scale meteorological data into multi-day episodes of quasi-stationary circulation. The episodes are subsequently grouped into a defined number of classes. A regional model is used to simulate the evolution of weather during the most typical episode of each class. These simulations consider the effects of the regional topography. Finally, the regional model results are statistically weighted with the climatological frequencies of the respective circulation classes in order to provide regional climate patterns. The statistical-dynamical downscaling procedure is applied to large-scale analyses for a 12-year climate period 1981–1992. The performance of the new method is demonstrated for winter precipitation in the Alpine region. With the help of daily precipitation analyses it was possible to validate the results and to assess the different sources of errors. It appeared that the main error originates from the regional model, whereas the error of the procedure itself was relatively unimportant. This new statistical-dynamical downscaling method turned out to be an efficient alternative to the commonly used method of nesting a regional model continuously within a general circulation model (dynamical downscaling). Received April 8, 1999 Revised July 30, 1999     

Statistical downscaling of hourly and daily climate scenarios for various meteorological variables in South-central Canada

Summary A regression-based methodology was used to downscale hourly and daily station-scale meteorological variables from outputs of large-scale general circulation models (GCMs). Meteorological variables include air temperature, dew point, and west–east and south–north wind velocities at the surface and three upper atmospheric levels (925, 850, and 500 hPa), as well as mean sea-level air pressure and total cloud cover. Different regression methods were used to construct downscaling transfer functions for different weather variables. Multiple stepwise regression analysis was used for all weather variables, except total cloud cover. Cumulative logit regression was employed for analysis of cloud cover, since cloud cover is an ordered categorical data format. For both regression procedures, to avoid multicollinearity between explanatory variables, principal components analysis was used to convert inter-correlated weather variables into uncorrelated principal components that were used as predictors. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response; for example, most hourly downscaling transfer functions could explain over 95% of the total variance for several variables (e.g. surface air temperature, dew point, and air pressure). Downscaling transfer functions were validated using a cross-validation scheme, and it was concluded that the functions for all weather variables used in the study are reliable. Performance of the downscaling method was also evaluated by comparing data distributions and extreme weather characteristics of downscaled GCM historical runs and observations during the period 1961–2000. The results showed that data distributions of downscaled GCM historical runs for all weather variables are significantly similar to those of observations. In addition, extreme characteristics of the downscaled meteorological variables (e.g. temperature, dew point, air pressure, and total cloud cover) were examined. Authors’ addresses: Chad Shouquan Cheng, Guilong Li, Qian Li, Atmospheric Science and Applications Unit, Meteorological Service of Canada Branch-Ontario, Environment Canada, 4905 Dufferin Street, Toronto, Ontario, Canada M3H 5T4; Heather Auld, Adaptation and Impacts Research Division, MSC Branch, Environment Canada, Toronto, Canada.     

Potential of continental-scale circulation for the determination of local daily surface variables

Summary This study deals with one aspect of statistical downscaling, viz. links between the continental-scale upper-air circulation and surface weather variables on a daily scale. The circulation-to-weather links are expressed in terms of multiple regression between either grid point values or intensities of circulation variability modes and weather elements, including temperature variables, relative humidity, cloudiness, sunshine duration, zonal and meridional wind components, precipitation, and atmospheric pressure. The upper-air circulation influences the surface pressure, temperature and zonal wind most strongly. The relative humidity, cloudiness and sunshine duration appear not to be connected with circulation in winter at all. The low-frequency part of circulation (i.e. processes with periods longer than 10 days) is most efficient in specifying surface weather variables. The circulation-to-weather links manifest considerable intra- and interdecadal variations, posing doubts on the applicability of the downscaling-from-circulation method of constructing climate change scenarios.With 11 Figures     

Shifts of means are not a proxy for changes in extreme winter temperatures in climate projections

Changes in the severity of extreme weather events under the influence of the enhanced greenhouse effect could have disproportionally large effects compared to changes in the mean climate. Here, we explored the meteorological circumstances of extremes and changes therein using two 49-member climate model ensembles for reference (1961–1990) and scenario (2051–2080) greenhouse-gas concentrations. We have focused on daily-mean surface-air temperatures over the Northern Hemisphere in January. Over large parts of the continents, changes in the one-in-10-year temperature events are influenced at least as much by changes in the shape of the probability distribution functions (PDFs) as by shifts in the mean. In coastal areas, this is largely attributable to changes in the large-scale circulation, for those types of extremes linked to infrequent wind directions. In other areas, the inhomogeneous mean warming, increasing inland and polewards, affects the tails of the local temperature PDFs. Temperature extremes in widely different regions were found to be linked by a large-scale circulation anomaly pattern, which resembles the Arctic Oscillation. In the scenario ensemble, this anomaly pattern favors its positive phase, leading to enhanced probabilities of westerly winds in a belt around the Northern Hemisphere.     

Transient simulations,empirical reconstructions and forcing mechanisms for the Mid-holocene hydrological climate in southern Patagonia

This study investigates the atmospheric circulation in transient climate simulations with a coupled atmosphere–ocean general circulation model (GCM) for the mid-Holocene (MH) period 7–4.5 ka BP driven with combinations of orbital, solar and greenhouse gas forcings. The focus is on southern South America. Statistical downscaling models are derived from observational data and applied to the simulations to estimate precipitation in south-eastern Patagonia during the MH. These estimates are compared with lake level estimates for Laguna Potrok Aike (LPA) from sediments. Relative to pre-industrial conditions (i.e. 1550–1850), which show extraordinarily high lake levels, the proxy-based reconstructed lake levels during the MH are lower. The downscaled simulated circulation differences indicate higher LPA precipitation during the MH from March to August, higher annual means, and reduced precipitation from September to February. Thus the reconstructed lower LPA lake levels can not be explained solely by the simulated precipitation changes. Possible reasons for this discrepancy are discussed. Based on proxy data from southern South America hypotheses have also been proposed on the latitudinal position of the southern hemispheric westerlies (SHWs). In agreement with some of these hypotheses our simulations show an increased seasonal cycle of the latitudinal position of the SHWs during the MH, which can be explained by the orbital forcing. The simulations also show stronger SHWs over southern Patagonia during austral summer and weaker SHWs during winter. The downscaling model associates weaker SHWs with increased precipitation in the LPA region. However, this relationship is only moderate, and therefore the downscaling model does not support the assumption of a strong link between mean SHWs and precipitation over south-eastern Patagonia, which is the basis of many proxy-based hypotheses about the SHWs.     

Impact of subgrid scale scheme on topography and landuse for better regional scale simulation of meteorological variables over the western Himalayas

An attempt is made to integrate subgrid scale scheme on the work of Dimri and Ganju (Pure Appl Geophys 167:1–24, 2007) to understand the overall nature of surface heterogeneity and landuse variability along with resolvable finescale micro/meso scale circulation over the Himalayan region, which is having different altitudes and orientations causing prevailing weather conditions to be complex. This region receives large amount of precipitation due to eastward moving low-pressure synoptic weather systems, called western disturbances, during winter season (December, January, February—DJF). Surface heterogeneity and landuse variability of the Himalayan region gives rise to numerous micro/meso scale circulation along with prevailing weather. Therefore, in the present work, a mosaic type parameterization of subgrid scale topography and landuse within a framework of a regional climate model (RegCM3) is extended to study interseasonal variability of surface climate during a winter season (October 1999–March 2000) of the work of Dimri and Ganju (Pure Appl Geophys 167:1–24, 2007). In this scheme, meteorological variables are disaggregated from the coarse grid to the fine grid, land surface calculations are then performed separately for each subgrid cell, and surface fluxes are calculated and reaggregated onto the coarse grid cell for input to the atmospheric model. By doing so, resolvable finescale structures due to surface heterogeneity and landuse variability at coarse grid are subjected to parameterize at regular finescale surface subgrid. Model simulations show that implementation of subgrid scheme presents more realistic simulation of precipitation and surface air temperature. Influence of topographic elevation and valleys is better represented in the scheme. Overall, RegCM3 with subgrid scheme provides more accurate representation of resolvable finescale atmospheric/surface circulations that results in explaining mean variability in a better way.     

Relationship between wind power production and North Atlantic atmospheric circulation over the northeastern Iberian Peninsula

The wind power generated during winter months 1999–2003 at several wind farms in the northeastern Iberian Peninsula is investigated through the application of a statistical downscaling. This allows for an improved understanding of the wind power variability and its relationship to the large scale atmospheric circulation. It is found that 97 % of the variability of this non-climatic variable is connected to changes in the atmospheric circulation. The methodological uncertainty associated with multiple configurations of the statistical downscaling method replicates well the observed variability of the wind power, an indication of the robustness of the methodology to changes in the model set up. In addition, the use of the statistical model is extended out of the observational period providing an estimation of the long-term variability of wind power throughout the twentieth century. The extended wind power reconstruction shows large inter-annual and multidecadal variability. Alternative approaches to calibrate the empirical downscaling model using actual wind power observations have also been investigated. They involve the estimation of wind power changes from downscaled wind values and make use of several transfer functions based on the linearity between wind and wind energy. The performance of the latter approaches is similar to the direct downscaling of wind power and may allow wind power production estimations even in the absence of historical wind turbine records. These results can be of great interest for deriving medium/long term impact-oriented energy assessments, especially when wind power observations are missing as well as in the context of climate change scenarios.     

Cross-Spectrum of Wind Speed for Meso-Gamma Scales in the Upper Surface Layer over South-Eastern Australia

Analytical expressions for the cross-spectrum of wind speed are developed for the stochastic simulation of wind power in south-eastern Australia. The expressions are valid for heights above the ground in the range 40–80 m, site separations of 1–30 km, and frequencies of (1/6)–3 cycles h⁻¹. The influence of site separation distance is taken into account, as are variables that are defined for blocks of time. These variables include the mean and standard deviation of wind speed and the mean wind direction. The parameters of the model equations are determined by non-linear least-squares regression with cross-validation over 10 years of wind measurements from 84 towers in south-eastern Australia.     

风电场风速降尺度预报方法对比分析

使用中尺度数值天气预报业务模式9 km和3 km分辨率的模式输出产品,分别应用小尺度模式CALMET模式和双线性插值(BLI)方法将预报风速进行降尺度处理,并对比预报风速和风塔观测资料。结果表明：WRF模式9 km分辨率的模式输出经过CALMET模式降尺度以后得到的风速预报效果比3 km分辨率的模式输出略好。同时,由于中尺度数值预报模式分辨率本身较高,使用BLI也可以得到较好的风速预报。将风速分为0 m·s^-1≤风速<5 m·s^-1,5 m·s^-1≤风速<10 m·s^-1和风速≥10 m·s^-1共3个等级,检验3个风速等级的预报偏差百分比得出,CALMET模式和BLI方法对10 m·s^-1以上的大风的预报效果相对较差;如何对大风预报进行订正对风速预报准确率的提高具有重要的意义。     

Urban–rural contrasts of meteorological parameters in Łódź

Summary Data from two automatic stations in Łódź (one urban and one rural) for the period 1997–2002 are analyzed to reveal urban–rural contrasts of such parameters as air temperature, relative humidity, water vapour pressure and wind speed. Under favourable weather conditions the highest temperature differences between the urban and rural station exceeds 8 °C. Relative humidity is lower in the town, sometimes by more than 40%. Water vapour pressure differences can be either positive (up to 5 hPa) or negative (up to −4 hPa). Wind speed at the urban station is on average lower by about 34% in night and 39% during daytime. Regression analysis shows that for rural winds lower than 1.13 m s⁻¹ urban winds can be stronger than rural speeds. Attention has also been paid to singularities in the course of the analyzed parameters over 24 hour periods. It is shown that the typical course of the urban heat island intensity under favourable conditions is similar in all season. Four stages of this course have been distinguished. Wind speed differences also seem to change in a typical way. Case studies show that humidity contrasts, unlike temperature, can evolve in different ways under fine weather conditions. Types of relative humidity evolution are proposed.     

Assessing the impact of climate changes on the potential yields of maize and paddy rice in Northeast China by 2050

Northeast China is the main crop production region in China, and future climate change will directly impact crop potential yields, so exploring crop potential yields under future climate scenarios in Northeast China is extremely critical for ensuring future food security. Here, this study projected the climate changes using 12 general circulation models (GCMs) under two moderate Representative Concentration Pathway (RCP) scenarios (RCP 4.5 and 6.0) from 2015 to 2050. Then, based on the Global Agro-ecological Zones (GAEZ) model, we explored the effect of climate change on the potential yields of maize and paddy rice in Northeast China during 2015–2050. The annual relative humidity increased almost throughout the Northeast China under two RCPs. The annual precipitation increased more than 400 mm in some west, east, and south areas under RCP 4.5, but decreased slightly in some areas under RCP 6.0. The annual wind speed increased over 2 m/s in the west region. The annual net solar radiation changes varied significantly with latitude, but the changes of annual maximum temperature and minimum temperature were closely related to the terrain. Under RCP 4.5, the average maize potential yield increased by 34.31% under the influence of climate changes from 2015 to 2050. The average rice potential yield increased by 16.82% from 2015 to 2050. Under RCP 6.0, the average maize and rice potential yields increased by 25.65% and 6.34% respectively. The changes of maize potential yields were positively correlated with the changes of precipitation, wind speed, and net solar radiation (the correlation coefficients were > 0.2), and negatively correlated with the changes of relative humidity, minimum and maximum temperature under two RCPs. The changes of rice potential yields were positively correlated with the changes of precipitation (correlation coefficient = 0.15) under RCP 4.5. Under RCP 6.0, it had a slight positive correlation with net solar radiation, relative humidity, and wind speed.