John S. Kimball

The Soil Moisture Active Passive (SMAP) Mission

The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Councils Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earths land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere. The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers. Soil moisture measurements are also directly applicable to flood assessment and drought monitoring. SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014-2015 time frame.

Recent decline in the global land evapotranspiration trend due to limited moisture supply

More than half of the solar energy absorbed by land surfaces is currently used to evaporate water. Climate change is expected to intensify the hydrological cycle and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land—a key diagnostic criterion of the effects of climate change and variability—remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm. In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.1 ± 1.0 millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Niño event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science.

Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations

The Moderate Resolution Spectroradiometer (MODIS) sensor has provided near real-time estimates of gross primary production (GPP) since March 2000. We compare four years (2000 to 2003) of satellite-based calculations of GPP with tower eddy CO2 flux-based estimates across diverse land cover types and climate regimes. We examine the potential error contributions from meteorology, leaf area index (LAI)/fPAR, and land cover. The error between annual GPP computed from NASAs Data Assimilation Offices (DAO) and tower-based meteorology is 28%, indicating that NASAs DAO global meteorology plays an important role in the accuracy of the GPP algorithm. Approximately 62% of MOD15-based estimates of LAI were within the estimates based on field optical measurements, although remaining values overestimated site values. Land cover presented the fewest errors, with most errors within the forest classes, reducing potential error. Tower-based and MODIS estimates of annual GPP compare favorably for most biomes, although MODIS GPP overestimates tower-based calculations by 20%-30%. Seasonally, summer estimates of MODIS GPP are closest to tower data, and spring estimates are the worst, most likely the result of the relatively rapid onset of leaf-out. The results of this study indicate, however, that the current MODIS GPP algorithm shows reasonable spatial patterns and temporal variability across a diverse range of biomes and climate regimes. So, while continued efforts are needed to isolate particular problems in specific biomes, we are optimistic about the general quality of these data, and continuation of the MOD17 GPP product will likely provide a key component of global terrestrial ecosystem analysis, providing continuous weekly measurements of global vegetation production

A continuous satellite‐derived global record of land surface evapotranspiration from 1983 to 2006

[1] We applied a satellite remote sensing-based evapotranspiration (ET) algorithm to assess global terrestrial ET from 1983 to 2006. The algorithm quantifies canopy transpiration and soil evaporation using a modified Penman-Monteith approach with biome-specific canopy conductance determined from the normalized difference vegetation index (NDVI) and quantifies open water evaporation using a Priestley-Taylor approach. These algorithms were applied globally using advanced very high resolution radiometer (AVHRR) GIMMS NDVI, NCEP/NCAR Reanalysis (NNR) daily surface meteorology, and NASA/GEWEX Surface Radiation Budget Release-3.0 solar radiation inputs. We used observations from 34 FLUXNET tower sites to parameterize an NDVI-based canopy conductance model and then validated the global ET algorithm using measurements from 48 additional, independent flux towers. Two sets of monthly ET estimates at the tower level, driven by in situ meteorological measurements and meteorology interpolated from coarse resolution NNR meteorology reanalysis, agree favorably (root mean square error (RMSE) = 13.0-15.3 mm month -1 ; R 2 = 0.80-0.84) with observed tower fluxes from globally representative land cover types. The global ET results capture observed spatial and temporal variations at the global scale and also compare favorably (RMSE = 186.3 mm yr -1 ; R 2 = 0.80) with ET inferred from basin-scale water balance calculations for 261 basins covering 61 % of the global vegetated area. The results of this study provide a relatively long term global ET record with well-quantified accuracy for assessing ET climatologies, terrestrial water, and energy budgets and long-term water cycle changes.

An improved method for estimating surface humidity from daily minimum temperature

Abstract Minimum daily air temperature ( T n ) is often used as a surrogate for mean daily dew point (TI,day) to estimate near-surface humidity. This method is unreliable under arid conditions where nightly minimum temperatures may remain well above the dew point. Daily meteorological data from 52 weather stations within the continental US and Alaska were evaluated. Daily differences between T d,day and T n were large in arid climates, with corresponding vapor pressure differences averaging between 0.8 and 1.2 kPa on an annual basis. Sites with semi-arid characteristics showed a large degree of seasonality in the results with average vapor pressure differences ranging from 0.1 to 0.6 kPa between winter and summer months. Sites in other climate regimes generally showed small differences between T n and T d,day over the entire year, corresponding to average vapor pressure differences of less than 0.3 kPa. An empirical model was developed to improve the accuracy of T n based humidity estimates using daily air temperature, annual precipitation and estimated daily potential evapotranspiration. The model reduced humidity estimation errors by up to 80% at semi-arid and arid sites and had minimal effects when T n based humidity estimates were relatively accurate. The ratio of annual precipitation to estimated annual evapotranspiration was useful for distinguishing sites where T n based humidity estimates were relatively accurate from sites where estimation errors were large. The results of this investigation provide a simple, more accurate method than T n for estimating humidity in arid and semi-arid regions using general weather station data.

The sensitivity of snowmelt processes to climate conditions and forest cover during rain-on-snow: a case study of the 1996 Pacific Northwest flood

A warm, very wet Pacific storm caused significant flooding in the Pacific Northwest during February 1996. Rapid melting of the mountain snow cover contributed to this flooding. An energy balance snowmelt model is used to simulate snowmelt processes during this event in the Central Cascade Mountains of Oregon. Data from paired open and forested experimental sites at locations at and just below the Pacific Crest were used to drive the model. The event was preceded by cold, stormy conditions that developed a significant snow cover down to elevations as low as 500 m in the Oregon Cascades. At the start of the storm, the depth of the snow cover at the high site (1142 m) was 1.97 m with a snow water equivalent (SWE) of 425 mm, while at the mid-site (968 m) the snow cover was 1.14 m with a SWE of 264 mm. During the 5‐6 day period of the storm the open high site received 349 mm of rain, lost 291 mm of SWE and generated 640 mm of runoA, leaving only 0.22 m of snow on the ground. The mid-site received 410 mm of rain, lost 264 mm of SWE to melt and generated 674 mm of runoA, completely depleting the snow cover. Simulations at adjacent forested sites showed significantly less snowmelt during the event. The snow cover under the mature forest at the high site lost only 44 mm of SWE during the event, generating 396 mm of runoA and leaving 0.69 m of snow. The model accurately simulated both snow cover depth and SWE during the development of the snow cover prior to the storm, and the depletion of the snow cover during the event. This analysis shows that because of the high temperature, humidity and relatively high winds in the open sites during the storm, 60‐90% of the energy for snowmelt came from sensible and latent heat exchanges. Because the antecedent conditions extended the snow cover to very low elevations in the basin, snowmelt generated by condensation during the event made a significant contribution to the flood. Lower wind speeds beneath the forest canopy during the storm reduced the magnitude of the turbulent exchanges at the snow surface, so the contribution of snowmelt to the runoA from forested areas was significantly less. This experiment shows the sensitivity of snowmelt processes to both climate and land cover, and illustrates how the forest canopy is coupled to the hydrological cycle in mountainous areas. #1998 John Wiley & Sons, Ltd.

Oak forest carbon and water simulations: model intercomparisons and evaluations against independent data

Models represent our primary method for integration of small-scale, process- level phenomena into a comprehensive description of forest-stand or ecosystem function. They also represent a key method for testing hypotheses about the response of forest ecosystems to multiple changing environmental conditions. This paper describes the eval- uation of 13 stand-level models varying in their spatial, mechanistic, and temporal com- plexity for their ability to capture intra- and interannual components of the water and carbon cycle for an upland, oak-dominated forest of eastern Tennessee. Comparisons between model simulations and observations were conducted for hourly, daily, and annual time steps. Data for the comparisons were obtained from a wide range of methods including: eddy covariance, sapflow, chamber-based soil respiration, biometric estimates of stand-level net primary production and growth, and soil water content by time or frequency domain reflectometry. Response surfaces of carbon and water flux as a function of environmental drivers, and a variety of goodness-of-fit statistics (bias, absolute bias, and model efficiency) were used to judge model performance. A single model did not consistently perform the best at all time steps or for all variables considered. Intermodel comparisons showed good agreement for water cycle fluxes, but considerable disagreement among models for predicted carbon fluxes. The mean of all model outputs, however, was nearly always the best fit to the observations. Not surprisingly, models missing key forest components or processes, such as roots or modeled soil water content, were unable to provide accurate predictions of ecosystem responses to short-term drought phenomenon. Nevertheless, an inability to correctly capture short-term physiolog- ical processes under drought was not necessarily an indicator of poor annual water and carbon budget simulations. This is possible because droughts in the subject ecosystem were of short duration and therefore had a small cumulative impact. Models using hourly time steps and detailed mechanistic processes, and having a realistic spatial representation of the forest ecosystem provided the best predictions of observed data. Predictive ability of all models deteriorated under drought conditions, suggesting that further work is needed to evaluate and improve ecosystem model performance under unusual conditions, such as drought, that are a common focus of environmental change discussions.

The hydrosphere State (hydros) Satellite mission: an Earth system pathfinder for global mapping of soil moisture and land freeze/thaw

The Hydrosphere State Mission (Hydros) is a pathfinder mission in the National Aeronautics and Space Administration (NASA) Earth System Science Pathfinder Program (ESSP). The objective of the mission is to provide exploratory global measurements of the earths soil moisture at 10-km resolution with two- to three-days revisit and land-surface freeze/thaw conditions at 3-km resolution with one- to two-days revisit. The mission builds on the heritage of ground-based and airborne passive and active low-frequency microwave measurements that have demonstrated and validated the effectiveness of the measurements and associated algorithms for estimating the amount and phase (frozen or thawed) of surface soil moisture. The mission data will enable advances in weather and climate prediction and in mapping processes that link the water, energy, and carbon cycles. The Hydros instrument is a combined radar and radiometer system operating at 1.26 GHz (with VV, HH, and HV polarizations) and 1.41 GHz (with H, V, and U polarizations), respectively. The radar and the radiometer share the aperture of a 6-m antenna with a look-angle of 39/spl deg/ with respect to nadir. The lightweight deployable mesh antenna is rotated at 14.6 rpm to provide a constant look-angle scan across a swath width of 1000 km. The wide swath provides global coverage that meet the revisit requirements. The radiometer measurements allow retrieval of soil moisture in diverse (nonforested) landscapes with a resolution of 40 km. The radar measurements allow the retrieval of soil moisture at relatively high resolution (3 km). The mission includes combined radar/radiometer data products that will use the synergy of the two sensors to deliver enhanced-quality 10-km resolution soil moisture estimates. In this paper, the science requirements and their traceability to the instrument design are outlined. A review of the underlying measurement physics and key instrument performance parameters are also presented.

Analysis of the Arctic System for Freshwater Cycle Intensification: Observations and Expectations

Abstract Hydrologic cycle intensification is an expected manifestation of a warming climate. Although positive trends in several global average quantities have been reported, no previous studies have documented broad intensification across elements of the Arctic freshwater cycle (FWC). In this study, the authors examine the character and quantitative significance of changes in annual precipitation, evapotranspiration, and river discharge across the terrestrial pan-Arctic over the past several decades from observations and a suite of coupled general circulation models (GCMs). Trends in freshwater flux and storage derived from observations across the Arctic Ocean and surrounding seas are also described. With few exceptions, precipitation, evapotranspiration, and river discharge fluxes from observations and the GCMs exhibit positive trends. Significant positive trends above the 90% confidence level, however, are not present for all of the observations. Greater confidence in the GCM trends arises through lowe...

A Remotely Sensed Global Terrestrial Drought Severity Index

Regional drought and flooding from extreme climatic events are increasing in frequency and severity, with significant adverse ecosocial impacts. Detecting and monitoring drought at regional to global scales remains challenging, despite the availability of various drought indices and widespread availability of potentially synergistic global satellite observational records. The authors have developed a method to generate a near-real-time remotely sensed drought severity index (DSI) to monitor and detect drought globally at 1-km spatial resolution and regular 8-day, monthly, and annual frequencies. The new DSI integrates and exploits information from current operational satellite-based terrestrial evapo-transpiration (ET) and vegetation greenness index [normalized difference vegetation index (NDVI)] products, which are sensitive to vegetation water stress. Specifically, this approach determines the annual DSI departure from its normal (2000–11) using the remotely sensed ratio of ET to potential ET (PET) and ...