•Maize yields are expected to be decreased following regional climate projections.•Yield decrease is more pronounced under RCP8.5 for 2031–2050.•Optimized planting dates (OPDs) may reduce negative ...impacts of climate change.•OPDs lead to higher potential yields compared to traditional methods.
The high intra-seasonal rainfall variability and the lack of adaptive capacities are the major limiting factors for rainfed agricultural production in smallholder farming systems across Sub-Saharan Africa. Therefore, the crop planting date, a low-cost agricultural management strategy aiming to alleviate crop water stress can contribute to enhance agricultural decision-making, particularly as a climate change adaptation strategy. By considering the crop water requirements throughout the crop growing cycle using a process-based crop model in conjunction with a fuzzy rule-based planting date approach, location-specific planting rules were derived for maize cropping in Burkina Faso (BF). Then, they were applied to regional future climate projections to derive optimized planting dates (OPDs) for the 2020s (2011–2030) and the 2040s (2031–2050), respectively. Based on potential maize yield simulations driven by climate change projections and planting dates, the OPD approach was compared with a well-established planting date method for West Africa and evaluated as a potential adaptation strategy for climate change. On average, the OPD approach achieved approximately +15% higher potential maize yield regardless of the regional climate model (RCM) and the period. However, the potential yield surpluses strongly decreased from the North to the South. Regarding climate change adaptation, the combined impact of climate change and the OPD approach has shown on average, a mean maize yield deviation between −23% and 34% in comparison to the 1989–2008 baseline period. Yield deviation is found to depend strongly on the RCM and location. The RCM ensemble mean yield for the period 2011–2050 revealed a maximum decrease of 8% compared to the baseline period. On the one hand, these findings highlight the potential of the OPDs as a crop management strategy but, on the other hand, it is apparent that farmers need to combine the OPDs with others suited farming practices to adequately respond to climate change.
This paper compares state-of-the-art atmospheric moisture tracking models. Such models are typically used to study the water component of coupled land and atmosphere models, in particular quantifying ...moisture recycling and the source-sink relations between evaporation and precipitation. There are several atmospheric moisture tracking methods in use. However, depending on the level of aggregation, the assumptions made and the level of detail, the performance of these methods may differ substantially. In this paper, we compare three methods. The RCM-tag method uses highly accurate 3-D water tracking (including phase transitions) directly within a regional climate model (online), while the other two methods (WAM and 3D-T) use a posteriori (offline) water vapour tracking. The original version of WAM is a single-layer model, while 3D-T is a multi-layer model, but both make use the "well-mixed" assumption for evaporation and precipitation. The a posteriori models are faster and more flexible, but less accurate than online moisture tracking with RCM-tag. In order to evaluate the accuracy of the a posteriori models, we tagged evaporated water from Lake Volta in West Africa and traced it to where it precipitates. It is found that the strong wind shear in West Africa is the main cause of errors in the a posteriori models. The number of vertical layers and the initial release height of tagged water in the model are found to have the most significant influences on the results. With this knowledge small improvements have been made to the a posteriori models. It appeared that expanding WAM to a 2-layer model, or a lower release height in 3D-T, led to significantly better results. Finally, we introduced a simple metric to assess wind shear globally and give recommendations about when to use which model. The "best" method, however, very much depends on the research question, the spatial extent under investigation, as well as the available computational power.
This paper presents a new Copula-based method for further downscaling regional climate simulations. It is developed, applied and evaluated for selected stations in the alpine region of Germany. Apart ...from the common way to use Copulas to model the extreme values, a strategy is proposed which allows to model continuous time series. As the concept of Copulas requires independent and identically distributed (iid) random variables, meteorological fields are transformed using an ARMA-GARCH time series model. In this paper, we focus on the positive pairs of observed and modelled (RCM) precipitation. According to the empirical copulas, significant upper and lower tail dependence between observed and modelled precipitation can be observed. These dependence structures are further conditioned on the prevailing large-scale weather situation. Based on the derived theoretical Copula models, stochastic rainfall simulations are performed, finally allowing for bias corrected and locally refined RCM simulations.
In this study, high‐resolution climate change data from the regional climate models COSMO‐CLM, HIRHAM, RegCM, and REMO were evaluated in the Greater Alpine Region (GAR; 4°W–19°W and 43°N–49°N) and ...three additional subareas of 1.5° by 1° in size. Evaluation statistics include mean temperature and precipitation, frequency of days with precipitation over 1 mm and over 15 mm, 90% quantile of the frequency distribution, and maximum number of consecutive dry days. The evaluation for the 1961–1990 period indicates that the models reproduce spatial precipitation patterns and the annual cycle. The mean precipitation domain bias varies between 11% and 40% in winter season and between −14.5% and 11% in summer. Larger errors are found for other statistics and in the various regions. No single best model could be identified comparing modeled precipitation characteristics with observational reference. The study shows that there is still high uncertainty in the expected climate change. Furthermore, future temperature and precipitation changes simulated with different SRES scenarios and calculated by different RCMs overlap. The temperature calculations for the period 2071–2100 related to the period 1961–1990 in the GAR area show an increase in the monthly mean 2m temperature of up to 4.8 K in summer. In the GAR area, a precipitation decrease of up to 29% in summer and precipitation increase of approximately 20% in the winter season is simulated. Summer and autumn temperatures are expected to increase more than winter and spring temperatures. Detailed analysis reveals that the different regional climate model runs based on different regional models, different driving global models and different emission scenarios show similar trends, but differ in the magnitude of the expected climate change signal. All models seem to agree on the increased frequency of high‐precipitation events in the winter season.
For the estimation of future climate conditions in the Jordan River region, the National Center for Atmospheric Research–Penn State University meteorology model in the versions 3.5 and 3.7 driven ...with boundary data from the Max‐Planck‐Institute for Meteorology and Hadley Centre global circulation models and the Special Report on Emission Scenarios A1B emission scenario has been used. The spatial resolution of the nested dynamic downscaling approach was 18.6 km, and the transient runs were performed for the period 1960–2099. The investigated statistics include mean precipitation, frequency and intensity of wet days and strong precipitation events, as well as mean temperature and heat wave duration index. The results show that the models satisfactorily reproduce the mean temperature and precipitation patterns. The comparison with the observational reference for the period 1961–1990 reveals a bias in the annual mean precipitation ranging from −20% to +17%, with an ensemble mean of −3%. The models show limitations in reproducing the precipitation seasonality. All models underestimate the wet day frequency and show differences in the strong precipitation events. The simulations of the future climate signal indicate an ensemble mean increase of the annual mean temperature of approximately 2.1 K in the period 2031–2060 and 3.7 K for the period 2070–2099 related to the 1961–1991 mean. In the same periods, the annual mean precipitation is simulated to decrease by approximately −11.5% and −20%, respectively, which means a reduction of expected water availability in the Jordan River region. All models show an increase of the heat wave duration index. A significant elevation dependence is present in the simulated future climate signal on both temperature and precipitation. The simulations show an increased coefficient of variation in annual precipitation, indicating that larger interannual precipitation variability can be expected in the future.
Key Points
Significant reduction of expected water availability in the Jordan River region
Significant elevation signal present in the simulated future climate
Increased coefficient of variation in simulated future annual precipitation
In this study, changes in the spatial and temporal patterns of climate extreme indices were analyzed. Daily maximum and minimum air temperature, precipitation, and their association with climate ...change were used as the basis for tracking changes at 50 meteorological stations in Iran over the period 1975–2010. Sixteen indices of extreme temperature and 11 indices of extreme precipitation, which have been quality controlled and tested for homogeneity and missing data, are examined. Temperature extremes show a warming trend, with a large proportion of stations having statistically significant trends for all temperature indices. Over the last 15 years (1995–2010), the annual frequency of warm days and nights has increased by 12 and 14 days/decade, respectively. The number of cold days and nights has decreased by 4 and 3 days/decade, respectively. The annual mean maximum and minimum temperatures averaged across Iran both increased by 0.031 and 0.059 °C/decade. The probability of cold nights has gradually decreased from more than 20 % in 1975–1986 to less than 15 % in 1999–2010, whereas the mean frequency of warm days has increased abruptly between the first 12-year period (1975–1986) and the recent 12-year period (1999–2010) from 18 to 40 %, respectively. There are no systematic regional trends over the study period in total precipitation or in the frequency and duration of extreme precipitation events. Statistically significant trends in extreme precipitation events are observed at less than 15 % of all weather stations, with no spatially coherent pattern of change, whereas statistically significant changes in extreme temperature events have occurred at more than 85 % of all weather stations, forming strongly coherent spatial patterns.
The Model for Prediction Across Scales (MPAS) is a novel set of Earth system simulation components and consists of an atmospheric model, an ocean model and a land-ice model. Its distinct features are ...the use of unstructured Voronoi meshes and C-grid discretisation to address shortcomings of global models on regular grids and the use of limited area models nested in a forcing data set, with respect to parallel scalability, numerical accuracy and physical consistency. This concept allows one to include the feedback of regional land use information on weather and climate at local and global scales in a consistent way, which is impossible to achieve with traditional limited area modelling approaches. Here, we present an in-depth evaluation of MPAS with regards to technical aspects of performing model runs and scalability for three medium-size meshes on four different high-performance computing (HPC) sites with different architectures and compilers. We uncover model limitations and identify new aspects for the model optimisation that are introduced by the use of unstructured Voronoi meshes. We further demonstrate the model performance of MPAS in terms of its capability to reproduce the dynamics of the West African monsoon (WAM) and its associated precipitation in a pilot study. Constrained by available computational resources, we compare 11-month runs for two meshes with observations and a reference simulation from the Weather Research and Forecasting (WRF) model. We show that MPAS can reproduce the atmospheric dynamics on global and local scales in this experiment, but identify a precipitation excess for the West African region. Finally, we conduct extreme scaling tests on a global 3 km mesh with more than 65 million horizontal grid cells on up to half a million cores. We discuss necessary modifications of the model code to improve its parallel performance in general and specific to the HPC environment. We confirm good scaling (70 % parallel efficiency or better) of the MPAS model and provide numbers on the computational requirements for experiments with the 3 km mesh. In doing so, we show that global, convection-resolving atmospheric simulations with MPAS are within reach of current and next generations of high-end computing facilities.
Key Points
Complex snow descriptions reproduce observed snow distribution
Energy balance method enhances modeling daily snowmelt and discharge variations
Simulating lateral snow transport improves ...runoff modeling in the catchment
Runoff generation in Alpine regions is typically affected by snow processes. Snow accumulation, storage, redistribution, and ablation control the availability of water. In this study, several robust parameterizations describing snow processes in Alpine environments were implemented in a fully distributed, physically based hydrological model. Snow cover development is simulated using different methods from a simple temperature index approach, followed by an energy balance scheme, to additionally accounting for gravitational and wind‐driven lateral snow redistribution. Test site for the study is the Berchtesgaden National Park (Bavarian Alps, Germany) which is characterized by extreme topography and climate conditions. The performance of the model system in reproducing snow cover dynamics and resulting discharge generation is analyzed and validated via measurements of snow water equivalent and snow depth, satellite‐based remote sensing data, and runoff gauge data. Model efficiency (the Nash‐Sutcliffe coefficient) for simulated runoff increases from 0.57 to 0.68 in a high Alpine headwater catchment and from 0.62 to 0.64 in total with increasing snow model complexity. In particular, the results show that the introduction of the energy balance scheme reproduces daily fluctuations in the snowmelt rates that trace down to the channel stream. These daily cycles measured in snowmelt and resulting runoff rates could not be reproduced by using the temperature index approach. In addition, accounting for lateral snow transport changes the seasonal distribution of modeled snowmelt amounts, which leads to a higher accuracy in modeling runoff characteristics.
Core Ideas
Pre‐alpine areas face more intense warming and extreme hydrological events than the global average.
Climate and land management change have far‐reaching impacts on ecosystem functions and ...services.
We have improved knowledge of water, energy, and matter exchange by long‐term observations and modeling.
Global change has triggered several transformations, such as alterations in climate, land productivity, water resources, and atmospheric chemistry, with far reaching impacts on ecosystem functions and services. Finding solutions to climate and land cover change‐driven impacts on our terrestrial environment is one of the most important scientific challenges of the 21st century, with far‐reaching interlinkages to the socio‐economy. The setup of the German Terrestrial Environmental Observatories (TERENO) Pre‐Alpine Observatory was motivated by the fact that mountain areas, such as the pre‐alpine region in southern Germany, have been exposed to more intense warming compared with the global average trend and to higher frequencies of extreme hydrological events, such as droughts and intense rainfall. Scientific research questions in the TERENO Pre‐Alpine Observatory focus on improved process understanding and closing of combined energy, water, C, and N cycles at site to regional scales. The main long‐term objectives of the TERENO Pre‐Alpine Observatory include the characterization and quantification of climate change and land cover–management effects on terrestrial hydrology and biogeochemical processes at site and regional scales by joint measuring and modeling approaches. Here we present a detailed climatic and biogeophysical characterization of the TERENO Pre‐Alpine Observatory and a summary of novel scientific findings from observations and projects. Finally, we reflect on future directions of climate impact research in this particularly vulnerable region of Germany.
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