Application Of Climate Models Research Articles

Measurement of surface energy balance over a humid tropical site at Ile-Ife, Nigeria

Surface energy balance studies for the humid tropical West African sub-region is rare in spite of its vital applications in agriculture, hydrological and climate modeling at local regional and global scale. In this study, the Eddy covariance technique (EC) has been used to study the partitioning of the available energy (R N -H G ) and energy balance closure over a humid tropical site at Ile-Ife, Nigeria (7.55°N; 4.56°E). The results showed that during the dry periods, the sensible heat flux was about 55-70% of the net radiation. However, for any wet day, the latent heat flux during the daytime was about 60 - 70% of the net radiation depending on the degree of the soil wetness and the prevailing atmospheric conditions. The available energy (R N -H G ) is larger (in most cases) than the sum of the turbulent fluxes (H S +H LE ) with a closure of energy balance varying between 65-93 %.

Measurement of surface energy balance over a humid tropical site at Ile-Ife, Nigeria

Surface energy balance studies for the humid tropical West African sub-region is rare in spite of its vital applications in agriculture, hydrological and climate modeling at local regional and global scale. In this study, the Eddy covariance technique (EC) has been used to study the partitioning of the available energy (RN-HG ) and energy balance closure over a humid tropical site at Ile-Ife, Nigeria (7.55 o N; 4.56 o E). The results showed that during the dry periods, the sensible heat flux was about 55–70% of the net radiation. However, for any wet day, the latent heat flux during the daytime was about 60 – 70% of the net radiation depending on the degree of the soil wetness and the prevailing atmospheric conditions. The available energy (RN-HG ) is larger (in most cases) than the sum of the turbulent fluxes (HS+HLE) with a closure of energy balance varying between 65-93 %.

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.

Uncertainty in ensemble forecasting of species distribution

AbstractSpecies distribution modelling has been widely applied in order to assess the potential impacts of climate change on biodiversity. Many methodological decisions, taken during the modelling process and forecasts, may, however, lead to a large variability in the assessment of future impacts. Using measures of species range change and turnover, the potential impacts of climate change on French stream fish species and assemblages were evaluated. Our main focus was to quantify the uncertainty in the projections of these impacts arising from four sources of uncertainty: initial datasets (Data), statistical methods [species distribution models (SDM)], general circulation models (GCM), and gas emission scenarios (GES). Several modalities of the aforementioned uncertainty sources were combined in an ensemble forecasting framework resulting in 8400 different projections. The variance explained by each source was then extracted from this whole ensemble of projections. Overall, SDM contributed to the largest variation in projections, followed by GCM, whose contribution increased over time equalling almost the proportion of variance explained by SDM in 2080. Data and GES had little influence on the variability in projections. Future projections of range change were more consistent for species with a large geographical extent (i.e., distribution along latitudinal or stream gradients) or with restricted environmental requirements (i.e., small thermal or elevation ranges). Variability in projections of turnover was spatially structured at the scale of France, indicating that certain particular geographical areas should be considered with care when projecting the potential impacts of climate change. The results of this study, therefore, emphasized that particular attention should be paid to the use of predictions ensembles resulting from the application of several statistical methods and climate models. Moreover, forecasted impacts of climate change should always be provided with an assessment of their uncertainty, so that management and conservation decisions can be taken in the full knowledge of their reliability.

A parameterized model of heat storage by lake sediments

A model of seasonal heat storage by lake sediments is proposed oriented at applications in climate modeling and at lake parameterization in numerical weather prediction. The computational efficiency is achieved by reformulating of the heat transfer problem as a set of ordinary differential equations for evolution of the temperature wave inside the upper sediment layer. Arising temperature and depth scales completely replace the conductivity of the sediment in the heat transfer equation and can be easily achieved from the lake water temperature observations without any data on the sediment thermal properties. The method is proposed for the scales estimation from the inverse solution of the model equations in special case of the constant water-sediment heat flux in ice-covered lakes. The method is tested on data from sediments of Lake Krasnoye, North-Western Russia. The long-term (1961–2002) modeling of temperature in German lakes Müggelsee and Heiligensee with a coupled one-dimensional model of lake water column and sediments has demonstrated an appreciable effect of the sediment heat storage on near-bottom temperatures in both lakes. Thus, incorporation of the sediment layer into lake temperature models can essentially improve, at low computational costs, the model performance, especially for shallow lakes. In addition, a better forecast of near-bottom temperature evolution on climatic scales can provide a better understanding of the response of lake benthic communities to global warming.

State‐of‐the‐art with regional climate models

AbstractRegional climate models are used by a large number of groups, for more or less all regions of the world. Regional climate models are complementary to global climate models. A typical use of regional climate models is to add further detail to global climate analyses or simulations, or to study climate processes in more detail than global models allow. The relationship between global and regional climate models is much akin to that of global and regional weather forecasting models. Over the past 20 years, the development of regional climate models has led to increased resolution, longer model runs, and steps towards regional climate system models. During recent years, community efforts have started to emerge in earnest, which can be expected to further advance the state‐of‐the‐art in regional climate modeling. Applications of regional climate models span both the past and possible future climates, facilitating climate impact studies, information and support to climate policy, and adaptation. Copyright © 2010 John Wiley & Sons, Inc.This article is categorized under: Climate Models and Modeling > Earth System Models

A Regional Climate Change Assessment Program for North America

There are two main uncertainties in determining future climate: the trajectories of future emissions of greenhouse gases and aerosols, and the response of the global climate system to any given set of future emissions [Meehl et al., 2007]. These uncertainties normally are elucidated via application of global climate models, which provide information at relatively coarse spatial resolutions. Greater interest in, and concern about, the details of climate change at regional scales has provided the motivation for the application of regional climate models, which introduces additional uncertainty [Christensen et al., 2007a].These uncertainties in fine‐scale regional climate responses, in contrast to uncertainties of coarser spatial resolution global models in which regional models are nested, now have been documented in numerous contexts [Christensen et al., 2007a] and have been found to extend to uncertainties in climate impacts [Wood et al., 2004; Oleson et al., 2007]. While European research in future climate projections has moved forward systematically to examine combined uncertainties from global and regional models [Christensen et al., 2007b], North American climate programs have lagged behind.

Northern African climate at the end of the twenty-first century: an integrated application of regional and global climate models

A method for simulating future climate on regional space scales is developed and applied to northern Africa. Simulation with a regional model allows for the horizontal resolution needed to resolve the region's strong meridional gradients and the optimization of parameteri- zations and land-surface model. The control simulation is constrained by reanalysis data, and realistically represents the present day climate. Atmosphere-ocean general circu- lation model (AOGCM) output provides SST and lateral boundary condition anomalies for 2081-2100 under a business-as-usual emissions scenario, and the atmospheric CO2 concentration is increased to 757 ppmv. A nine- member ensemble of future climate projections is gene- rated by using output from nine AOGCMs. The consistency of precipitation projections for the end of the twenty-first century is much greater for the regional model ensemble than among the AOGCMs. More than 77% of ensemble members produce the same sign rainfall anomaly over much of northern Africa. For West Africa, the regional model projects wetter conditions in spring, but a mid- summer drought develops during June and July, and the heat stoke risk increases across the Sahel. Wetter conditions resume in late summer, and the likelihood of flooding increases. The regional model generally projects wetter conditions over eastern Central Africa in June and drying during August through September. Severe drought impacts parts of East Africa in late summer. Conditions become wetter in October, but the enhanced rainfall does not compensate for the summertime deficit. The risk of heat stroke increases over this region, although the threat is not projected to be as great as in the Sahel.

Evaluation of high-resolution simulations of daily-scale temperature and precipitation over the United States

Extreme climate events have been increasing over much of the world, and dynamical models predict further increases in response to enhanced greenhouse forcing. We examine the ability of a high-resolution nested climate model, RegCM3, to capture the statistics of daily-scale temperature and precipitation events over the conterminous United States, using observational and reanalysis data for comparison. Our analyses reveal that RegCM3 captures the pattern of mean, interannual variability, and trend in the tails of the daily temperature and precipitation distributions. However, consistent biases do exist, including wet biases in the topographically-complex regions of the western United States and hot biases in the southern and central United States. The biases in heavy precipitation in the western United States are associated with excessively strong surface and low-level winds. The biases in daily-scale temperature and precipitation in the southcentral United States are at least partially driven by biases in circulation and moisture fields. Further, the areas of agreement and disagreement with the observational data are not intuitive from analyzing the simulated mean seasonal temperature and precipitation fields alone. Our evaluation should enable more informed application and improvement of high-resolution climate models for the study of future changes in socially- and economically-relevant temperature and precipitation events.

Sensitivity of bare soil albedo to surface soil moisture on the moraine of the Zongo glacier (Bolivia)

The dependence of bare soil albedo on soil water content is investigated using in situ data collected on the moraine of an Andean glacier (Bolivia). This study demonstrates a high negative correlation between the two variables that is best approximated by an exponential function, in agreement with previous studies. More importantly, the average snow‐free albedo value during the rainy season is 40% lower than during the dry season (0.16 vs. 0.26). These results are relevant for climate and land surface modeling applications, where bare soil albedo is often considered as a constant parameter.

Running climate models on grids using G-R ex

Compute grids are used widely in many areas of environmental science, but there has been limited uptake of grid computing by the climate modelling community, partly because the characteristics of many climate models make them difficult to use with popular grid middleware systems. In particular, climate models usually produce large volumes of output data, and running them also involves complicated workflows implemented as shell scripts. A new grid middleware system that is well suited to climate modelling applications is presented in this paper. Grid Remote Execution (G-Rex) allows climate models to be deployed as Web services on remote computer systems and then launched and controlled as if they were running on the user's own computer. Output from the model is transferred back to the user while the run is in progress, to prevent it from accumulating on the remote system and to allow the user to monitor the model. G-Rex has a representational state transfer (REST) architectural style, featuring a Java client program that can easily be incorporated into existing scientific workflow scripts. Some technical details of G-Rex are presented, with examples of its use by climate modellers.

Arctic land hydrothermal sensitivity under warming: Idealized off‐line evaluation of a physical terrestrial scheme in a global climate model

A series of idealized one‐dimensional off‐line sensitivity experiments for the Arctic hydroclimate under a transitional warming environment were conducted to investigate the impact of different internal mechanisms and external forcing (excessive water input) in a physical terrestrial scheme, Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO), used in a global climate model. The scheme has freeze/thaw processes and was run with the column depth greater than 50 m. The inclusion of the top organic layers and the physically based parameterization of soil hydrothermal properties led to a realistic seasonal amplitude of a subsurface thermal regime and mitigated the warming and deepening of the maximum active layer thickness (ALT). After a 6‐K warming over 100 years, ALT increases by 67% (from 9.6 to 16 cm) with the top organic layers and increases by more than a factor of 2 for the default mineral layers (122% from 45 cm to 1.0 m). With the more realistic thermal property profile, the physically based parameterizations projected ALT after the 100‐year warming to be about a half of what the original parameterization did (2.0 against 3.5 m). Finer soil layer thickness near the surface had impacts on the near‐surface wetness and the energy and water exchange between the atmosphere and also showed greater tolerance to the misconfiguration of porosity profile, whose global‐scale distribution is poorly known for global climate model applications. With much wetter forcing, water infiltration kept the soil close to the saturation, degradation of the frozen state proceeded faster, and the adaptation back to a drier condition occurred on a decadal time scale.

The use of the Climate-science Computational End Station (CCES) development and grand challenge team for the next IPCC assessment: an operational plan

The grand challenge of climate change science is to predict future climates based on scenarios of anthropogenic emissions and other changes resulting from options in energy and development policies. Addressing this challenge requires a Climate Science Computational End Station consisting of a sustained climate model research, development, and application program combined with world-class DOE leadership computing resources to enable advanced computational simulation of the Earth system. This project provides the primary computer allocations for the DOE SciDAC and Climate Change Prediction Program. It builds on the successful interagency collaboration of the National Science and the U.S. Department of Energy in developing and applying the Community Climate System Model (CCSM) for climate change science. It also includes collaboration with the National Aeronautics and Space Administration in carbon data assimilation and university partners with expertise in high-end computational climate research.

Advance in application of regional climate models in China

Regional climate models have become the powerful tools for simulating regional climate and its change process and have been widely used in China. Using regional climate models, some research results have been obtained on the following aspects: 1) the numerical simulation of East Asian monsoon climate, including exceptional monsoon precipitation, summer precipitation distribution, East Asian circulation, multi-year climate average condition, summer rain belt and so on; 2) the simulation of arid climate of the western China, including thermal effect of the Qing- hai-Tibet Plateau, the plateau precipitation in the Qilian Mountains; and the impacts of greenhouse effects (CO2 dou- bling) upon climate in the western China; and 3) the simulation of the climate effect of underlying surface changes, in- cluding the effect of soil on climate formation, the influence of terrain on precipitation, the effect of regional soil deg- radation on regional climate, the effect of various underlying surfaces on regional climate, the effect of land-sea con- trast on the climate formulation, the influence of snow cover over the plateau regions on the regional climate, the effect of vegetation changes on the regional climate, etc. In the process of application of regional climate models, the prefer- ences of the models are improved so that better simulation results are gotten. At last, some suggestions are made about the application of regional climate models in regional climate research in the future.

Developing a spatially continuous 1 km surface albedo data set over North America from Terra MODIS products

Surface albedo is an important factor governing the surface radiation budget and is critical in modeling the exchange of energy, water and carbon between Earth surface and atmosphere. Global satellite observation systems have been providing surface albedo products periodically. However, due to factors associated with weather, sensors and algorithms, albedo products from satellite observations often have many gaps and low quality pixels. Take the North American Moderate Resolution Imaging Spectroradiometer (MODIS) albedo products (MOD43B3) from 2000–2004 as an example, only 31.3% of pixels were retrieved with fully data‐driven inversions and labeled with the highest possible mandatory quality flag of “good”. Conversely, 13.3% did not contain any valid retrieval and the remaining values vary in quality from moderate full inversions to poor retrievals utilizing a back‐up algorithm. This indicates considerable potential for product improvement through the use of gap‐filling algorithms. Our objective is to generate spatially and temporally continuous albedo products through gap filling and filtering based on multiyear observations and high quality neighboring pixels. The resultant albedo data set substantially improves the time series of surface albedo, especially in the winter, when for some areas the MODIS albedo products may have no retrievals. A comparison with field measurements shows that the filtered albedo correlates well with the measured surface albedo with the root mean squared errors (RMSEs) around 0.064 for snow‐free pixels and 0.078 overall. The generated spectral albedo, broadband albedo and monthly albedo can be used for climate modeling and data assimilation applications.

Improving the representation of radiation interception and photosynthesis for climate model applications

Improving the representation of radiation interception and photosynthesis for climate model applications

Aggregation of vertical flow in the vadose zone with auto- and cross-correlated hydraulic properties

Summary Quantifying water flow across larger areas of the vadose zone has applications in water resources management and climate modeling. The nonlinearity of unsaturated flow and the variability of vadose zone parameters make it difficult, if not impossible, to accurately simulate near-surface water content and flux with large-scale models. Monte Carlo simulations of one-dimensional infiltration and evaporation were conducted with the Richards equation to simulate moisture content and flux in a heterogeneous field according to the streamtube concept. A set of 126 retention curves and saturated hydraulic conductivities from the UNSODA database was used to generate random fields of hydraulic parameters with pre-defined auto- and cross-correlation. Two stochastic parameters were used: the retention shape factor, ln mn , and either the retention scale parameter θ s or ln h G or the saturated hydraulic conductivity, ln K s . Infiltration is mostly governed by ln K s . The evaporative flux is strongly determined by the “structural” parameter ln h G and also by the “textural” parameter ln mn . The water content in the upper part of the soil depends mostly on ln mn and somewhat on θ s . Cross-correlations all resulted in clusters with consistently low or high water contents and moisture fluxes. Aggregation to obtain results at larger scales was done by a posteriori averaging of local results. This procedure is a convenient benchmark for large-scale modeling approaches. In an example of a priori aggregation, effective retention parameters were optimized to synthetic retention curves for the larger pixel scale and subsequently used in the Richards equation. The amount of infiltrated water was overestimated by up to 40%, large parts of the upper profile were erroneously predicted to be saturated. Although effective hydraulic properties have been used successfully in evaporation studies, considerable errors, which increased with pixel size, also occurred for evaporation. The stream tube modeling offers a convenient and accurate, albeit mundane, approach to elucidate the role of hydraulic properties and to obtain large-scale hydrological data.

Improving the representation of radiation interception and photosynthesis for climate model applications

The Joint UK Land Environment Simulator (JULES) (which is based on MetOffice Surface Exchange Scheme MOSES), the land surface scheme of the Hadley Centre General Circulation Models (GCM) has been improved to contain an explicit description of light interception for different canopy levels, which consequently leads to a multilayer approach to scaling from leaf to canopy level photosynthesis. We test the improved JULES model at a site in the Amazonian rainforest by comparing against measurements of vertical profiles of radiation through the canopy, eddy covariance measurements of carbon and energy fluxes, and also measurements of carbon isotopic fractionation from top canopy leaves.Overall, the new light interception formulation improves modelled photosynthetic carbon uptake compared to the standard big leaf approach used in the original JULES formulation. Additional model improvement was not significant when incorporating more realistic vertical variation of photosynthetic capacity. Even with the improved representation of radiation interception, JULES simulations of net carbon uptake underestimate eddy covariance measurements by 14%. This discrepancy can be removed by either increasing the photosynthetic capacity throughout the canopy or by explicitly including light inhibition of leaf respiration. Along with published evidence of such inhibition of leaf respiration, our study suggests this effect should be considered for inclusion in other GCMs.

Bulk microphysics parametrization of ice fraction for application in climate models

AbstractUsing in situ aircraft measurements of cloud microphysical properties collected in extratropical stratiform clouds during several field programs, a parametrization of the ice‐particle spectrum that includes small ice particles has been developed. This parametrization has been tested using a single prognostic equation developed by Tremblay et al. (1996) for application in a regional model. The addition of small ice‐particles significantly increases the vapour deposition‐rate when the natural atmosphere is assumed to be water saturated, and thus enhances the glaciation of simulated mixed‐phase cloud via the Bergeron–Findeisen process without significantly affecting the other cloud microphysical processes such as riming and particle‐sedimentation rates. After the water vapour pressure in mixed‐phase cloud was modified, based on the scheme of Lord et al. (1984), by weighting the saturation water vapour pressure with ice fraction, it was possible to simulate a more stable mixed‐phase cloud. It was also noted that the ice‐particle concentration (maximum dimension L > 100 µm ) in mixed‐phase cloud is lower on average by a factor of three, and, consequently, the parametrization should be corrected for this effect. After accounting for this effect, the parametrized ice‐fraction agreed well with observation. Copyright © 2006 Royal Meteorological Society

A framework for regional modeling of past climates

The methods of reconstructing ancient climate information from the rock record are summarized, and the climate forcing factors that have been active at global and regional scales through Earth history are reviewed. In this context, the challenges and approaches to modeling past climates by using a regional climate model are discussed. A significant challenge to such modeling efforts arises if the time period of interest occurred prior to the past ∼3–5 million years, at which point land–sea distributions and topography markedly different from present must be specified at the spatial resolution required by regional climate models. Creating these boundary conditions requires a high degree of geologic knowledge, and also depends greatly upon the global climate model driving conditions. Despite this and other challenges, regional climate models represent an important and unique tool for paleoclimate investigations. Application of regional climate models to paleoclimate studies may provide another way to assess the overall performance of regional climate models.