Large-scale Climate Models Research Articles

An investigation into the performance of four cloud droplet activation parameterisations

Abstract. Cloud droplet number concentration prediction is central to large-scale weather and climate modelling. The benchmark cloud parcel model calculation of aerosol particle growth and activation, by diffusion of vapour to aerosol particles in a rising parcel of air experiencing adiabatic expansion, is too computationally expensive for use in large-scale global models. Therefore the process of activation of aerosol particles into cloud droplets is parameterised with an aim to strike the optimum balance between numerical expense and accuracy. We present a detailed systematic evaluation of three cloud droplet activation parameterisations that are widely used in large-scale models and one recent update. In all cases, it is found that there is a tendency to overestimate the fraction of activated aerosol particles when the aerosol particle "median diameter" is large (between 250 and 2000 nm) in a single lognormal mode simulation. This is due to an infinite "effective simulation time" of the parameterisations compared to a prescribed simulation time in the parcel model. This problem arises in the parameterisations because it is assumed that a parcel of air rises to the altitude where maximum supersaturation occurs, regardless of whether this altitude is above the cloud top. Such behaviour is problematic because, in some cases, large aerosol can completely suppress the activation of drops. In some cases when the "median diameter" is small (between 5 and 250 nm) in a single lognormal mode the fraction of activated drops is underestimated by the parameterisations. Secondly, it is found that in dual-mode cases there is a systematic tendency towards underestimation of the fraction of activated drops, which is due to the methods used by the parameterisations to approximate the sink of water vapour.

Heat flux calculations for Mackenzie and Yukon Rivers

This study analyzes long-term (40–60 years) discharge and water temperature records collected near the basin outlets of the Yukon and Mackenzie Rivers. It defines seasonal cycles of discharge, water temperature (WT), and heat flux (HF) for the basins, and compares their main features to understand their similarity and difference. Both rivers have similar hydrographs, i.e. low flows in winter and high discharge in summer, with the peak flood in June due to snowmelt runoff. Mackenzie River has many large lakes and they sustain the higher base flows over the fall/winter season. Mackenzie basin is large with high precipitation, thus producing 50% more discharge than the Yukon River to the Arctic Ocean. The WT regimes are also similar between the two rivers. Yukon River WT is about 2–3 °C warmer than the Mackenzie over the open water months. Both rivers have the highest WT in the mid summer and they transport large amount of heat to the polar ocean system. Yukon River monthly HF is lower by 10–60% than the Mackenzie mainly due to smaller discharge. Mackenzie River heat transport peaks in July, while the Yukon HF reaches the maximum in June and July. These results provide critical knowledge of river thermal condition and energy transport to the northern seas. They are useful for large-scale climate and ocean model development and validation, and climate/hydrology change research in the northern regions.

Transformation of small-scale meteorological tsunami due to terrain complexity on the western coast of Korea

ABSTRACT Ha, T., Choi, J.-Y., Yoo, J., Chun, I., Shim, J., 2014. Transformation of small-scale meteorological tsunami due to terrain complexity on western coast of Korea. In: Green, A.N. and Cooper, J.A.G. (eds.), Proceedings 13th International Coastal Symposium (Durban, South Africa), Journal of Coastal Research, Special Issue No. 70, pp. 284–289, ISSN 0749-0208. On the 4th of May, 2008, a meteorological tsunami occurred in the Yellow Sea and abnormal waves were observed at Jukdo Island, located on the western coast of Boryeong, Korea. The event attracted the attention of many engineers and scientists because it caused extreme waves only at Jukdo Island, unlike the meteorological tsunami event in 2007, which caused abnormal extreme waves along the western coast of Korea. In general, a meteorological tsunami can be forecasted using large-scale meteorological observation data and climate modeling systems. However, a small-scale meteorological tsunami like the event that occurred in 2008, which can cause ab...

Evaluating sub-national building-energy efficiency policy options under uncertainty: Efficient sensitivity testing of alternative climate, technological, and socioeconomic futures in a regional integrated-assessment model

Improving the energy efficiency of building stock, commercial equipment, and household appliances can have a major positive impact on energy use, carbon emissions, and building services. Sub-national regions such as the U.S. states wish to increase energy efficiency, reduce carbon emissions, or adapt to climate change. Evaluating sub-national policies to reduce energy use and emissions is difficult because of the large uncertainties in socioeconomic factors, technology performance and cost, and energy and climate policies. Climate change itself may undercut such policies. However, assessing all of the uncertainties of large-scale energy and climate models by performing thousands of model runs can be a significant modeling effort with its accompanying computational burden. By applying fractional–factorial methods to the GCAM-USA 50-state integrated-assessment model in the context of a particular policy question, this paper demonstrates how a decision-focused sensitivity analysis strategy can greatly reduce computational burden in the presence of uncertainty and reveal the important drivers for decisions and more detailed uncertainty analysis.

Regional Climate Modeling over the Maritime Continent. Part II: New Parameterization for Autoconversion of Convective Rainfall

Abstract This paper describes a new method for parameterizing the conversion of convective cloud liquid water to rainfall (“autoconversion”) that can be used within large-scale climate models, and evaluates the new method using the Regional Climate Model, version 3 (RegCM3), coupled to the land surface scheme Integrated Biosphere Simulator (IBIS). The new method is derived from observed distributions of cloud water content and is constrained by observations of cloud droplet characteristics and climatological rainfall intensity. This new method explicitly accounts for subgrid variability with respect to cloud water density and is independent of model resolution, making it generally applicable for large-scale climate models. This work builds on the development of a new parameterization method for convective cloud fraction, which was described in Part I. Simulations over the Maritime Continent using the Emanuel convection scheme show significant improvement in model performance, not only with respect to convective rainfall but also in shortwave radiation, net radiation, and turbulent surface fluxes of latent and sensible heat, without any additional modifications made to the simulation of those variables. Model improvements are demonstrated over a 19-yr validation period as well as a shorter 4-yr evaluation. Model performance with the Grell convection scheme is not similarly improved and reasons for this outcome are discussed. This work illustrates the importance of representing observed subgrid-scale variability in diurnally varying convective processes for simulations of the Maritime Continent region.

Regional Climate Modeling over the Maritime Continent. Part I: New Parameterization for Convective Cloud Fraction

Abstract This paper describes a new method for parameterizing convective cloud fraction that can be used within large-scale climate models, and evaluates the new method using the Regional Climate Model, version 3 (RegCM3), coupled to the land surface scheme Integrated Biosphere Simulator (IBIS). The horizontal extent of convective cloud cover is calculated by utilizing a relationship between the simulated amount of convective cloud water and typical observations of convective cloud water density. This formulation not only provides a physically meaningful basis for the simulation of convective cloud cover, but it is also spatially and temporally variable and independent of model resolution, rendering it generally applicable for large-scale climate models. Simulations over the Maritime Continent show that the new method allows for simulation of an essential convective–radiative feedback, which was absent in the existing version of RegCM3–IBIS, such that moist convection not only responds to diurnal variability at the earth’s surface but also impacts the solar radiation received at the surface via cumulus cloud production. The impact on model performance was mixed, but it is considered that appropriate representation of the convective–radiative feedback and improved physical realism resulting from the new cloud fraction parameterization will likely have positive benefits elsewhere. The role of convective rainfall production in the convective–radiative feedback and a new parameterization for convective autoconversion are addressed in Part II of this paper series.

Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?

Abstract. In hydrological climate-change impact studies, regional climate models (RCMs) are commonly used to transfer large-scale global climate model (GCM) data to smaller scales and to provide more detailed regional information. Due to systematic and random model errors, however, RCM simulations often show considerable deviations from observations. This has led to the development of a number of correction approaches that rely on the assumption that RCM errors do not change over time. It is in principle not possible to test whether this underlying assumption of error stationarity is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well correction methods perform for conditions different from those used for calibration with the relatively simple differential split-sample test. For five Swedish catchments, precipitation and temperature simulations from 15 different RCMs driven by ERA40 (the 40 yr reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF)) were corrected with different commonly used bias correction methods. We then performed differential split-sample tests by dividing the data series into cold and warm respective dry and wet years. This enabled us to cross-evaluate the performance of different correction procedures under systematically varying climate conditions. The differential split-sample test identified major differences in the ability of the applied correction methods to reduce model errors and to cope with non-stationary biases. More advanced correction methods performed better, whereas large deviations remained for climate model simulations corrected with simpler approaches. Therefore, we question the use of simple correction methods such as the widely used delta-change approach and linear transformation for RCM-based climate-change impact studies. Instead, we recommend using higher-skill correction methods such as distribution mapping.

Hydrologic implications of errors in bias-corrected regional reanalysis data for west central Florida

Summary This study investigated the limitations associated with using dynamically-downscaled, bias-corrected reanalysis data (i.e. regional reanalysis data) to predict hydrologic behavior of low-relief rainfall driven systems using an integrated surface/groundwater model. Four different sets of global reanalysis data (NCEP/NCAR-R1, NCEP-DOE-R2, ERA40, and 20CR) that were previously downscaled using two RCMs (MM5 and RSM) were obtained, bias-corrected on a daily basis using the CDF-mapping approach, and used to drive an integrated hydrologic model (INTB) that was previously calibrated and verified for the Tampa Bay region. All raw dynamically-downscaled reanalysis datasets accurately estimated the annual cycle of daily maximum and minimum temperature, except the NCEP/NCAR R1+MM5 data which consistently underestimated daily maximum temperature. All raw regional reanalysis precipitation data significantly overestimated precipitation, particularly for the dry season. Bias-correction using the CDF-mapping approach effectively removed biases in the temporal mean and standard deviation of both the daily precipitation and temperature predictions. Biases in the mean monthly and mean annual precipitation totals were removed by CDF-mapping on a daily basis, but the standard deviation of the monthly and annual precipitation totals were not accurately reproduced. Furthermore inaccuracies in actual daily precipitation time series aggregated into monthly and annual rainfall total time series that showed significant and temporally persistent errors. Precipitation timing errors produced by regional reanalysis data were propagated and enhanced by non-linear streamflow generation, groundwater flow and storage processes in the hydrologic model and produced significant errors in both actual and mean daily, monthly and annual streamflow and groundwater level predictions. These results show that improvement in large-scale reanalysis products and regional climate models may be required before dynamically downscaled bias-corrected reanalysis data can be used as a surrogate for observational data in hydrologic model applications for low-relief, rainfall driven systems.

Overflow Waters at the Iceland–Faroe Ridge Observed in Multiyear Seaglider Surveys

Abstract This paper presents new observations of the overflow waters downstream of the Faroe Bank Channel (FBC) and the Iceland–Faroe Ridge (IFR). Between 2006 and 2009, over 17 400 hydrographic profiles were collected during quarterly deployments in the region by autonomous gliders, providing previously unrealized spatial resolution to observations downstream of the FBC. Observations show that the second sill of the FBC coincides with the largest changes in the overflow plume, including significant thinning, widening, and entrainment. Between the second sill and a topographic feature 75 km downstream, the plume bifurcates with the densest portion (65% of the transport), descending below 1000 m. On the IFR, near-bottom velocities are directed alongslope with speeds averaging 21.5 cm s−1. Observations indicate that 80% of baroclinic velocities associated with mesoscale variability of the overflow plume are smaller than the alongslope topographically induced circulation. Evidence of overflow is found at all locations on the Atlantic flank of the IFR. However, the meridionally oriented portion at 13°W has anomalously warm bottom water and divides FBC and eastern IFR overflow from overflow found in the Western Valley. Individual Seaglider sections identify IFR overflow in a narrow current (8–14 km wide) along the Iceland shelf with a mean transport of 0.43 Sv (1 Sv ≡ 106 m3 s−1) with significant variability from days to weeks. A lower-bound estimate of 0.8 Sv of total IFR overflow is presented. These results provide constraints on regional models that inform the representation of this crucial, yet underresolved, region in large-scale ocean and climate models.

Special Issue on Advances in the Fundamental Study, Numerical Simulation, and Prediction of Hydrologic Processes in River Basins

The impacts of environmental changes, such as climate and land use–land cover changes, on the hydrologic system have increased at regional and global scales in the last few decades. As a result, environmental consequences in large-scale river basins, like floods and droughts, and the management of these basins require the understanding of contributing watersheds within the basin. Studying hydrologic processes and numerical simulation and prediction of these processes under the influence of climate changes and human activities within the river basin are crucial to many environmental aspects, such as the development and utilization of water resources, and mitigation of the disasters of floods and droughts. Many programs and projects, such as “The Impact of Climate Change on the Hydrologic Cycle and Assessment of Water Resource Security in the Region of Yellow River, Huaihe River, and Hai River,” have been aimed to evaluate the impact of climate change on the hydrologic cycle, to examine climate change– driven mechanisms of floods and droughts, and to assess the influence of climate change on regional water resources. This special issue consists of a series of papers in different basins. The topics covered by the papers include (1) the assessment of impacts of climate change on hydrologic processes, such as streamflow and sediment transport; (2) the hydrologic response (e.g., evapotranspiration and runoff) to the land use–land cover changes; (3) model development and application of hydrologic simulations; and (4) hydrologic analysis and simulation with the integrated remote sensing and advanced techniques. These studies present recent advances in the fundamental study, numerical simulation, and prediction of hydrologic processes in river basins. The interactive links among the land-surface and subsurface processes and atmospheric processes are very important in current research efforts to understand how perturbed climate conditions (both human-induced and natural) can modify basin-scale hydrologic systems and, in turn, how the modified hydrologic conditions can influence the climatic system. Although many advances in these research areas have been achieved, the comprehensively integrated basin approach with data assimilation and analysis (e.g., satellite and radar data) is required to address various aspects of impacts of environmental changes (e.g., climate change and land use–land cover changes) on the hydrologic system, such as the understanding of hydrologic processes in response to these changes, the representation of basin-scale processes in large-scale climate and hydrology models, and application of these further scientific advances in the basin-scale decision making.

Effects of tropical instability wave (TIW)-induced surface wind feedback in the tropical Pacific Ocean

Tropical instability waves (TIWs) arise from oceanic instability in the eastern tropical Pacific and Atlantic Oceans, having a clear atmospheric signature that results in coupled atmosphere-ocean interactions at TIW scales. In this study, the extent to which TIW-induced surface wind feedback influences the ocean is examined using an ocean general circulation model (OGCM). The TIW-induced wind stress (sTIW) part is diagnostically determined using an empirical sTIW model from sea surface temperature (SST) fields simulated in the OGCM. The interactively represented TIW wind tends to reduce TIW activity in the ocean and influence the mean state, with largest impacts during TIW active periods in fall and winter. In December, the interactive sTIW forcing induces a surface cooling (an order of -0.1 to -0.3 C), an increased heat flux into the ocean, a shallower mixed layer and a weakening of the South Equatorial Current in the eastern equatorial Pacific. Additionally, the TIW wind effect yields a pronounced latitudinal asymmetry of sea level field across the equator, and a change to upper thermal structure, characterized by a surface cooling and a warming below in the thermocline, leading to a decreased temperature gradient between the mixed layer and the thermocline. Pro- cesses responsible for the sTIW-induced cooling effects are analyzed. Vertical mixing and meridional advection are the two terms in the SST budget that are dominantly affected by the TIW wind feedback: the cooling effect from the vertical mixing on SST is enhanced, with the maximum induced cooling in winter; the warming effect from the meridional advection is reduced in July-October, but enhanced in November-December. Additional experiments are per- formed to separate the relative roles the affected surface momentum and heat fluxes play in the cooling effect on SST. This ocean-only modeling work indicates that the effect of TIW-induced wind feedback is small but not negligible, and may need to be adequately taken into account in large-scale climate modeling.

A new methodology for weighting high-resolution model simulations to project future rainfall in the Middle East

A divergence metric was used to combine 4 high-resolution climate models to gen- erate more reliable simulations of future rainfall. The approach is based on the assumption that the use of multiple models (an ensemble) is superior to the use of a single model, even if one of the models is shown to better capture past trends. Such an approach is especially useful in areas with steep climatic gradients, where large-scale climate models are not effective in capturing oro- graphic and local effects. We applied the methodology to the Middle East, and specifically to Israel, where climate shifts from arid to humid temperate occur over a distance of around 400 km. Model weights were determined by calculating the similarity between the probability distribu- tions of the models and those of the historical data using the Jenson-Shannon divergence metric. These weights were then applied to future model projections. Annual amounts of rainfall, num- bers of wet days and numbers of 3 d wet spells were analyzed. Compared with observed data, the weighted ensemble outperformed the equal weights ensemble, which outperformed the best model. For the northern and central stations, average annual amounts of rainfall decreased in both near- and far-future periods, with most of the change occurring at the peak and in the left-hand tail and less change in the right-hand tail of the probability distribution. This, combined with the change in the right-hand tail of the distribution in numbers of wet spells in the near future, sug- gests that the decline in overall rainfall will be higher than the corresponding decline in extreme events; or in other words even though there will be less rainfall, the extreme events will remain, and even possibly increase. In the south, a mixed trend of slightly increasing median amounts of rainfall and slightly decreasing extreme events is projected.

Evaluation of Surface Flux Parameterizations with Long-Term ARM Observations

Abstract Surface momentum, sensible heat, and latent heat fluxes are critical for atmospheric processes such as clouds and precipitation, and are parameterized in a variety of models ranging from cloud-resolving models to large-scale weather and climate models. However, direct evaluation of the parameterization schemes for these surface fluxes is rare due to limited observations. This study takes advantage of the long-term observations of surface fluxes collected at the Southern Great Plains site by the Department of Energy Atmospheric Radiation Measurement program to evaluate the six surface flux parameterization schemes commonly used in the Weather Research and Forecasting (WRF) model and three U.S. general circulation models (GCMs). The unprecedented 7-yr-long measurements by the eddy correlation (EC) and energy balance Bowen ratio (EBBR) methods permit statistical evaluation of all six parameterizations under a variety of stability conditions, diurnal cycles, and seasonal variations. The statistical analyses show that the momentum flux parameterization agrees best with the EC observations, followed by latent heat flux, sensible heat flux, and evaporation ratio/Bowen ratio. The overall performance of the parameterizations depends on atmospheric stability, being best under neutral stratification and deteriorating toward both more stable and more unstable conditions. Further diagnostic analysis reveals that in addition to the parameterization schemes themselves, the discrepancies between observed and parameterized sensible and latent heat fluxes may stem from inadequate use of input variables such as surface temperature, moisture availability, and roughness length. The results demonstrate the need for improving the land surface models and measurements of surface properties, which would permit the evaluation of full land surface models.

Assessment of the utility of dynamically-downscaled regional reanalysis data to predict streamflow in west central Florida using an integrated hydrologic model

The goal of this study was to evaluate the ability of dynamically downscaled reanalysis data to reproduce local-scale spatiotemporal precipitation and temperature data needed to accurately predict streamflow in the Tampa Bay region of west central Florida. In particular, the Florida State University Center for Ocean-Atmospheric Prediction Studies CLARReS10 data (NCEP DOE 2 reanalysis data (R2) downscaled to 10-km over the Southeast USA using the Regional Spectral Model (RSM) were evaluated against locally available observed precipitation and temperature data and then used to drive an integrated hydrologic model that was previously calibrated for the Tampa Bay region. Resulting streamflow simulations were evaluated against observed data and previously calibrated model results. Results showed that the raw downscaled reanalysis predictions accurately reproduced the seasonal trends of mean daily minimum temperature, maximum temperature and precipitation, but generally overestimated the monthly mean and standard deviation of daily precipitation. Biases in the temporal mean and standard deviation of daily precipitation and temperature predictions were effectively removed using a CDF-mapping approach; however, errors in monthly precipitation totals remained after bias correction. Monthly streamflow simulation error statistics indicated that the accuracy of the streamflow produced by the bias-corrected downscaled reanalysis data was satisfactory (i.e., sufficient for seasonal to decadal planning), but that the accuracy of the streamflow produced by the raw downscaled reanalysis data was unsatisfactory for water resource planning purposes. The findings of this study thus indicate that further improvement in large-scale reanalysis data and regional climate models is needed before dynamically downscaled reanalysis data can be used directly (i.e. without bias correction with local data) to drive hydrologic models. However, bias-corrected dynamically downscaled data show promise for extending local historic climate observation records for hydrologic simulations. Furthermore, results of this study indicate that similarly bias-corrected dynamically downscaled retrospective and future GCM projections should be suitable for assessing potential hydrologic impacts of future climate change in the Tampa Bay region.

Retrieving latent heating vertical structure from cloud and precipitation profiles—Part II: Deep convective and stratiform rain processes

An exploratory study on physical based latent heat (LH) retrieval algorithm is conducted by parameterizing the physical linkages between observed cloud and precipitation profiles to the major processes of phase change of atmospheric water. Specifically, rain is segregated into three rain types: warm, convective, and stratiform rain, based on their dynamical and thermodynamical characteristics. As the second of series, both convective and stratiform rain LH algorithms are presented and evaluated here. For convective and stratiform rain, the major LH-related microphysical processes including condensation, deposition, evaporation, sublimation, and freezing–melting are parameterized with the aid of Cloud Resolving Model (CRM) simulations. The condensation and deposition processes are parameterized in terms of rain formation processes through the precipitation formation theory. LH associated with the freezing–melting process is relatively small and is assumed to be a fraction of total condensation and deposition LH. The evaporation and sublimation processes are parameterized for three unsaturated scenarios: rain out of the cloud body, clouds at cloud boundary and clouds and rain in downdraft region. The evaluation or self-consistency test indicates the retrievals capture the major features of LH profiles and reproduce the double peaks at right altitudes. The LH products are applicable at various stages of cloud system life cycle for high-resolution models, as well as for large-scale climate models.

Afternoon rain more likely over drier soils

Land surface properties, such as vegetation cover and soil moisture, influence the partitioning of radiative energy between latent and sensible heat fluxes in daytime hours. During dry periods, soil-water deficit can limit evapotranspiration, leading to warmer and drier conditions in the lower atmosphere. Soil moisture can influence the development of convective storms through such modifications of low-level atmospheric temperature and humidity, which in turn feeds back on soil moisture. Yet there is considerable uncertainty in how soil moisture affects convective storms across the world, owing to a lack of observational evidence and uncertainty in large-scale models. Here we present a global-scale observational analysis of the coupling between soil moisture and precipitation. We show that across all six continents studied, afternoon rain falls preferentially over soils that are relatively dry compared to the surrounding area. The signal emerges most clearly in the observations over semi-arid regions, where surface fluxes are sensitive to soil moisture, and convective events are frequent. Mechanistically, our results are consistent with enhanced afternoon moist convection driven by increased sensible heat flux over drier soils, and/or mesoscale variability in soil moisture. We find no evidence in our analysis of a positive feedback--that is, a preference for rain over wetter soils-at the spatial scale (50-100 kilometres) studied. In contrast, we find that a positive feedback of soil moisture on simulated precipitation does dominate in six state-of-the-art global weather and climate models--a difference that may contribute to excessive simulated droughts in large-scale models.

Differences between downscaling with spectral and grid nudging using WRF

Abstract. Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.

Climatic physical snowpack properties for large-scale modeling examined by observations and a physical model

Here we have conducted an integral study using site observations and a model with detailed snow dynamics, to examine the capability of the model for deriving a simple relationship between the density and thermal conductivity of the snowpack within different climatic zones used in large-scale climate modeling. Snow and meteorological observations were conducted at multiple sites in different climatic regions (two in Interior Alaska, two in Japan). A series of thermal conductivity measurements in snow pit observations done in Alaska provided useful information for constructing the relationship. The one-dimensional snow dynamics model, SNOWPACK, simulated the evolution of the snowpack and compared observations between all sites. Overall, model simulations tended to underestimate the density and overestimate the thermal conductivity, and failed to foster the relationship evident in the observations from the current and previous research. The causes for the deficiency were analyzed and discussed, regarding a low density of the new snow layer and a slow compaction rate. Our working relationships were compared to the equations derived by previous investigators. Discrepancy from the regression for the melting season observations in Alaska was found in common.

Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources

Freshwater resources are becoming increasingly limited in many parts of the world, and decision makers are demanding new tools for monitoring water availability and rates of consumption. Remotely sensed thermal-infrared imagery collected by Landsat provides estimates of land-surface temperature that allow mapping of evapotranspiration (ET) at the spatial scales at which water is being used. This paper explores the utility of moderate-resolution thermal satellite imagery in water resource management. General modeling techniques for using land-surface temperature in mapping the surface energy balance are described, including methods developed to safeguard ET estimates from expected errors in the remote sensing inputs. Examples are provided of how remotely sensed maps of ET derived from Landsat thermal imagery are being used operationally by water managers today: in monitoring water rights, negotiating interstate compacts, estimating water-use by invasive species, and in determining allocations for agriculture, urban use, and endangered species protection. Other applications include monitoring drought and food insecurity, and evaluation of large-scale land-surface and climate models. To better address user requirements for high-resolution, time-continuous ET data, novel techniques have been developed to improve the spatial resolution of Landsat thermal-band imagery and temporal resolution between Landsat overpasses by fusing information from other wavebands and satellites. Finally, a strategy for future modification to the Landsat program is suggested, improving our ability to track changes in water use due to changing climate and growing population. The long archive of global, moderate resolution TIR imagery collected by the Landsat series is unmatched by any other satellite program, and will continue to be an invaluable asset to better management of our earth's water resources.

Detection of open water dynamics with ENVISAT ASAR in support of land surface modelling at high latitudes

Abstract. Wetlands are generally accepted as being the largest but least well quantified single source of methane (CH4). The extent of wetland or inundation is a key factor controlling methane emissions, both in nature and in the parameterisations used in large-scale land surface and climate models. Satellite-derived datasets of wetland extent are available on the global scale, but the resolution is rather coarse (>25 km). The purpose of the present study is to assess the capability of active microwave sensors to derive inundation dynamics for use in land surface and climate models of the boreal and tundra environments. The focus is on synthetic aperture radar (SAR) operating in C-band since, among microwave systems, it has comparably high spatial resolution and data availability, and long-term continuity is expected. C-band data from ENVISAT ASAR (Advanced SAR) operating in wide swath mode (150 m resolution) were investigated and an automated detection procedure for deriving open water fraction has been developed. More than 4000 samples (single acquisitions tiled onto 0.5° grid cells) have been analysed for July and August in 2007 and 2008 for a study region in Western Siberia. Simple classification algorithms were applied and found to be robust when the water surface was smooth. Modification of input parameters results in differences below 1 % open water fraction. The major issue to address was the frequent occurrence of waves due to wind and precipitation, which reduces the separability of the water class from other land cover classes. Statistical measures of the backscatter distribution were applied in order to retrieve suitable classification data. The Pearson correlation between each sample dataset and a location specific representation of the bimodal distribution was used. On average only 40 % of acquisitions allow a separation of the open water class. Although satellite data are available every 2–3 days over the Western Siberian study region, the irregular acquisition intervals and periods of unsuitable weather suggest that an update interval of 10 days is more realistic for this domain. SAR data availability is currently limited. Future satellite missions, however, which aim for operational services (such as Sentinel-1 with its C-band SAR instrument), may provide the basis for inundation monitoring for land surface and climate modelling applications.