Modern-Era Retrospective Analysis For Research And Applications Research Articles

Diagnosing Warm Frontal Cloud Formation in a GCM: A Novel Approach Using Conditional Subsetting

Abstract This study analyzes characteristics of clouds and vertical motion across extratropical cyclone warm fronts in the NASA Goddard Institute for Space Studies general circulation model. The validity of the modeled clouds is assessed using a combination of satellite observations from CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis. The analysis focuses on developing cyclones, to test the model's ability to generate their initial structure. To begin, the extratropical cyclones and their warm fronts are objectively identified and cyclone-local fields are mapped into a vertical transect centered on the surface warm front. To further isolate specific physics, the cyclones are separated using conditional subsetting based on additional cyclone-local variables, and the differences between the subset means are analyzed. Conditional subsets are created based on 1) the transect clouds and 2) vertical motion; 3) the strength of the temperature gradient along the warm front, as well as the storm-local 4) wind speed and 5) precipitable water (PW). The analysis shows that the model does not generate enough frontal cloud, especially at low altitude. The subsetting results reveal that, compared to the observations, the model exhibits a decoupling between cloud formation at high and low altitudes across warm fronts and a weak sensitivity to moisture. These issues are caused in part by the parameterized convection and assumptions in the stratiform cloud scheme that are valid in the subtropics. On the other hand, the model generates proper covariability of low-altitude vertical motion and cloud at the warm front and a joint dependence of cloudiness on wind and PW.

Regional Climate and Variability of NASA MERRA and Recent Reanalyses: U.S. Summertime Precipitation and Temperature

AbstractReanalyses have increasingly improved resolution and physical representation of regional climate and so may provide useful data in many regional applications. These data are not observations, however, and their limitations and uncertainties need to be closely investigated. The ability of reanalyses to reproduce the seasonal variations of precipitation and temperature over the United States during summer, when model forecasts have characteristically weak forecast skill, is assessed. Precipitation variations are reproduced well over much of the United States, especially in the Northwest, where ENSO contributes to the large-scale circulation. Some significant biases in the seasonal mean do exist. The weakest regions are the Midwest and Southeast, where land–atmosphere interactions strongly affect the physical parameterizations in the forecast model. In particular, the variance of the Modern-Era Retrospective Analysis for Research and Applications (MERRA) is too low (extreme seasonal averages are weak), and the variability of the Interim ECMWF Re-Analysis (ERA-Interim) is affected by spurious low-frequency trends. Surface temperature is generally robust among the reanalyses examined, though; reanalyses that assimilate near-surface observations have distinct advantages. Observations and forecast error from MERRA are used to assess the reanalysis uncertainty across U.S. regions. These data help to show where the reanalysis is realistically replicating physical processes, and they provide guidance on the quality of the data and needs for further development.

Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing

Abstract In situ soil moisture measurements from 2007 to 2010 for 196 stations from five networks across the world (United States, France, Spain, China, and Australia) are used to determine the reliability of three soil moisture products: (i) a revised version of the ECMWF Interim Re-Analysis (ERA-Interim; ERA-Land); (ii) a revised version of the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis from NASA (MERRA-Land); and (iii) a new, microwave-based multisatellite surface soil moisture dataset (SM-MW). Evaluation of the time series and anomalies from a moving monthly mean shows a good performance of the three products in capturing the annual cycle of surface soil moisture and its short-term variability. On average, correlations (95% confidence interval) are 0.66 (±0.038), 0.69 (±0.038), and 0.60 (±0.061) for ERA-Land, MERRA-Land, and SM-MW. The two reanalysis products also capture the root-zone soil moisture well; on average, correlations are 0.68 (±0.035) and 0.73 (±0.032) for ERA-Land and MERRA-Land, respectively. Global trends analysis for 1988–2010 suggests a decrease of surface soil moisture contents (72% of significant trends are negative, i.e., drying) for ERA-Land and an increase in surface soil moisture (59% of significant trends are positive, i.e., wetting) for MERRA-Land. As the spatial extent and fractions of significant trends in both products differ, the trend reflected in the majority of grid points within different climate classes was investigated and compared to that of SM-MW. The latter is dominated by negative significant trends (73.2%) and is more in line with ERA-Land. For both reanalysis products, trends for the upper layer of soil are confirmed in the root-zone soil moisture (first meter of soil).

Moist dynamics of active/break cycle of Indian summer monsoon rainfall from NCEPR2 and MERRA reanalysis

ABSTRACTThe vertically integrated moisture and moist static energy (MSE) budgets during the active and break phases of Indian summer monsoon are studied using National Centers for Environmental Prediction Reanalysis2 (NCEPR2) and Modern Era Retrospective Analysis for Research and Applications (MERRA) data. The different parameterization procedures adopted by the reanalysis models will impose inevitable differences between the reanalysis products, which will make an impact on their intraseasonal oscillation (ISO) also. At the same time, the agreement of the moisture budget terms of the reanalysis products has enabled us to come up with a common moisture mechanism responsible for the ISO. Estimates of the moisture budget indicates that the rainfall anomalies over the Indian monsoon region in both active and break phases are mainly (70–80%) balanced by local moisture divergence. The remaining 20–25% is accounted by the horizontal moisture advection. The moisture advection is found to lead the rainfall by 8–9 d, while there is no lead‐lag relationship is observed between the moisture divergence and rainfall. The anomalous moisture advection from the northwest pacific initiates the active phase of Indian monsoon and the dry air advection from the northwest of India after the onset of active convection sets off the break conditions. MSE budget over India is balanced by MSE divergence and moisture advection, indicating that the variation in MSE is mainly governed by moisture in the intraseasonal timescales. A positive covariance between radiative and surface fluxes and precipitation anomalies indicates that these flux terms slowdown the variation of MSE during active/break phase and may help to extend the ISO. The anomalous moisture advection has a major role in northward propagation of ISO also. A large residual of MSE budget during onset of ISO is the main caveat of the reanalysis products and it is the major constrain in the present analysis also.

Estimation of gross primary production over the terrestrial ecosystems in China

Gross primary production (GPP) is of significant importance for the terrestrial carbon budget and climate change, but large uncertainties in the regional estimation of GPP still remain over the terrestrial ecosystems in China. Eddy covariance (EC) flux towers measure continuous ecosystem-level exchange of carbon dioxide (CO2) and provide a promising way to estimate GPP. We used the measurements from 32 EC sites to examine the performance of a light use efficiency model (i.e., EC-LUE) at various ecosystem types, including 23 sites in China and 9 sites in adjacent areas with the similar climate environments. No significant systematic error was found in the EC-LUE model predictions, which explained 79% and 62% of the GPP variation at the validation sites with C3 and C4 vegetation, respectively. Regional patterns of GPP at a spatial resolution of 10km×10km from 2000 to 2009 were determined using the MERRA (Modern Era Retrospective-analysis for Research and Applications) reanalysis dataset and MODIS (MODerate resolution Imaging Spectroradiometer). China's terrestrial GPP decreased from southeast toward the northwest, with the highest values occurring over tropical forests areas, and the lowest values in dry regions. The annual GPP of land in China varied between 5.63PgC and 6.39PgC, with a mean value of 6.04PgC, which accounted for 4.90–6.29% of the world's total terrestrial GPP. The GPP densities of most vegetation types in China such as evergreen needleleaf forests, deciduous needleleaf forests, mixed forests, woody savannas, and permanent wetlands were much higher than the respective global GPP densities. However, a high proportion of sparsely vegetated area in China resulted in the overall low GPP. The inter-annual variability in GPP was significantly influenced by air temperature (R2=0.66, P<0.05), precipitation (R2=0.71, P<0.05), and normalized difference vegetation index (NDVI) (R2=0.83, P<0.05), respectively.

Tropical Precipitation Variability and Convectively Coupled Equatorial Waves on Submonthly Time Scales in Reanalyses and TRMM

Abstract Tropical precipitation characteristics are investigated using the Tropical Rainfall Measuring Mission (TRMM) 3-hourly estimates, and the result is compared with five reanalyses including the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim), Modern Era Retrospective Analysis for Research and Applications (MERRA), National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (NCEP1), NCEP–U.S. Department of Energy (DOE) reanalysis (NCEP2), and NCEP–Climate Forecast System Reanalysis (CFSR). Precipitation characteristics are evaluated in terms of the mean, convectively coupled equatorial wave activity, frequency characteristics, diurnal cycle, and seasonality of regional precipitation variability associated with submonthly scale waves. Generally the latest reanalyses such as ERA-Interim, MERRA, and CFSR show better performances than NCEP1 and NCEP2. However, all the reanalyses are still different from observations. Besides the positive mean bias in the reanalyses, a spectral analysis revealed that the reanalyses have overreddened spectra with persistent rainfall. MERRA has the most persistent rainfall, and CFSR appears to have the most realistic variability. The diurnal cycle in NCEP1 is extremely exaggerated relative to TRMM. The low-frequency waves with the period longer than 3 days are relatively well represented in ERA-Interim, MERRA, and CFSR, but all the reanalyses have significant deficiencies in representing convectively coupled equatorial waves and variability in the high-frequency range.

Diabatic Heating Profiles in Recent Global Reanalyses

Abstract Diabatic heating profiles are extremely important to the atmospheric circulation in the tropics and therefore to the earth’s energy and hydrological cycles. However, their global structures are poorly known because of limited information from in situ observations. Some modern global reanalyses provide the temperature tendency from the physical processes. Their proper applications require an assessment of their accuracy and uncertainties. In this study, diabatic heating profiles from three recent global reanalyses [ECMWF Interim Re-Analysis (ERA-Interim), Climate Forecast System Reanalysis (CFSR), and Modern Era Retrospective Analysis for Research and Applications (MERRA)] are compared to those derived from currently available sounding observations in the tropics and to each other in the absence of the observations. Diabatic heating profiles produced by the reanalyses match well with those based on sounding observations only at some locations. The three reanalyses agree with each other better in the extratropics, where large-scale condensation dominates the precipitation process in data assimilation models, than in the tropics, where cumulus parameterization dominates. In the tropics, they only agree with each other in gross features, such as the contrast between the ITCZs over different oceans. Their largest disagreement is the number and level of heating peaks in the tropics. They may produce a single, double, or triple heating peak at a given location. It is argued that cumulus parameterization cannot be the sole source of the disagreement. Implications of such disagreement are discussed.

Statistical Characterization of Arctic Polar-Night Jet Oscillation Events

AbstractA novel diagnostic tool is presented, based on polar-cap temperature anomalies, for visualizing daily variability of the Arctic stratospheric polar vortex over multiple decades. This visualization illustrates the ubiquity of extended-time-scale recoveries from stratospheric sudden warmings, termed here polar-night jet oscillation (PJO) events. These are characterized by an anomalously warm polar lower stratosphere that persists for several months. Following the initial warming, a cold anomaly forms in the middle stratosphere, as does an anomalously high stratopause, both of which descend while the lower-stratospheric anomaly persists. These events are characterized in four datasets: Microwave Limb Sounder (MLS) temperature observations; the 40-yr ECMWF Re-Analysis (ERA-40) and Modern Era Retrospective Analysis for Research and Applications (MERRA) reanalyses; and an ensemble of three 150-yr simulations from the Canadian Middle Atmosphere Model. The statistics of PJO events in the model are found to agree very closely with those of the observations and reanalyses.The time scale for the recovery of the polar vortex following sudden warmings correlates strongly with the depth to which the warming initially descends. PJO events occur following roughly half of all major sudden warmings and are associated with an extended period of suppressed wave-activity fluxes entering the polar vortex. They follow vortex splits more frequently than they do vortex displacements. They are also related to weak vortex events as identified by the northern annular mode; in particular, those weak vortex events followed by a PJO event show a stronger tropospheric response. The long time scales, predominantly radiative dynamics, and tropospheric influence of PJO events suggest that they represent an important source of conditional skill in seasonal forecasting.

Sensitivity study on the role of Western Ghats in simulating the Asian summer monsoon characteristics

The Advanced Research Weather Research and Forecasting (AR-WRF) model is used to study the influence of Western Ghats situated along the west cost of peninsular India in the mean characteristics of the Asian summer monsoon (ASM) through numerical simulations. A control simulation (CTRL) is carried out using 11-year (2000–2010) mean initial and lateral boundary conditions from the ERA-Interim reanalysis to simulate the mean atmospheric features of the ASM. The Modern-Era retrospective analysis for research and applications (MERRA) data along with the Tropical Rainfall Measuring Mission (TRMM, 3B42 daily rainfall) data are used to validate the CTRL simulation. The simulated dynamical features and precipitation characteristics during the ASM period agree well with the MERRA reanalysis and TRMM observations. In order to examine the role of Western Ghats on the mean characteristics of the ASM, a sensitivity simulation (NoWG) is carried out with orography reduced to surface over a domain bound between 5°–28°N and 72°–90°E, keeping all other conditions unchanged. This sensitivity analysis showed an enhancement in the low level monsoon flow over the Indian Ocean and peninsular India in the absence of Western Ghats. The prominent up-draft over the west coast of peninsular India observed in the CTRL simulation also decrease in the absence of Western Ghats. The simulated rainfall show a considerable decrease over the west coast and an enhancement over the east coast of peninsular India in the absence of Western Ghats. These simulations clearly depict the importance of Western Ghats in the circulation dynamics and rainfall features during the ASM period.

Sea Surface Temperature–Precipitation Relationship in Different Reanalyses

Abstract The focus of this investigation is how the relationship at intraseasonal time scales between sea surface temperature and precipitation (SST–P) varies among different reanalyses. The motivation for this work was spurred by a recent report that documented that the SST–P relationship in Climate Forecast System Reanalysis (CFSR) was much closer to that in the observation than it was for the older generation of reanalyses [i.e., NCEP–NCAR reanalysis (R1) and NCEP–Department of Energy (DOE) reanalysis (R2)]. Further, the reason was attributed either to the fact that the CFSR is a partially coupled reanalysis, while R1 and R2 are atmospheric-alone reanalyses, or that R1 and R2 use the observed weekly-averaged SST. The authors repeated the comparison of the SST–P relationship among R1, R2, and CFSR, as well as two recent generations of atmosphere-alone reanalyses, the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and the ECMWF Re-Analysis Interim (ERA-Interim). The results clearly demonstrate that the differences in the SST–P relationship at intraseasonal time scales across different reanalyses are not due to whether the reanalysis system is coupled or atmosphere alone, but are due to the specification of different SSTs. The SST–P relationship in different reanalyses, when computed against a single SST for the benchmark, demonstrates a relationship that is common across all of the reanalyses and observations.

Evaluation of Satellite-Based and Reanalysis Precipitation Data in the Tropical Pacific

AbstractGlobal precipitation monitoring is essential for understanding the earth’s water and energy cycle. Therefore, usage of satellite-based precipitation data is necessary where in situ data are rare. In addition, atmospheric-model-based reanalysis data feature global data coverage and offer a full catalog of atmospheric variables including precipitation. In this study, two model-based reanalysis products, the interim reanalysis by the European Centre for Medium-Range Weather Forecasts (ERA-Interim) and NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA), as well as two satellite-based datasets obtained by the Global Precipitation Climatology Centre (GPCP) and Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) are evaluated. The evaluation is based on monthly precipitation in the tropical Pacific Ocean during the time period 1989–2005. Rain-gauge atoll station data provided by the Pacific Rainfall Database (PACRAIN) are used as ground-based reference. It is shown that the analyzed precipitation datasets offer temporal correlations ranging from 0.7 to 0.8 for absolute amounts and from 0.6 to 0.75 for monthly anomalies. Average monthly deviations are in the range of 20%–30%. GPCP offers the highest correlation and lowest monthly deviations with reference to PACRAIN station data. The HOAPS precipitation data perform in the range of the reanalysis precipitation datasets. In high native spatial resolution, HOAPS reveals deficiencies owing to its relatively sparse temporal coverage. This result emphasizes that temporal coverage is critical for controlling the performance of precipitation monitoring. Both reanalysis products show similar systematic behaviors in overestimating small and medium precipitation amounts and underestimating high amounts.

Where Does the Irrigation Water Go? An Estimate of the Contribution of Irrigation to Precipitation Using MERRA

Abstract Irrigation is an important human activity that may impact local and regional climate, but current climate model simulations and data assimilation systems generally do not explicitly include it. The European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) shows more irrigation signal in surface evapotranspiration (ET) than the Modern-Era Retrospective Analysis for Research and Applications (MERRA) because ERA-Interim adjusts soil moisture according to the observed surface temperature and humidity while MERRA has no explicit consideration of irrigation at the surface. But, when compared with the results from a hydrological model with detailed considerations of agriculture, the ET from both reanalyses show large deficiencies in capturing the impact of irrigation. Here, a back-trajectory method is used to estimate the contribution of irrigation to precipitation over local and surrounding regions, using MERRA with observation-based corrections and added irrigation-caused ET increase from the hydrological model. Results show substantial contributions of irrigation to precipitation over heavily irrigated regions in Asia, but the precipitation increase is much less than the ET increase over most areas, indicating that irrigation could lead to water deficits over these regions. For the same increase in ET, precipitation increases are larger over wetter areas where convection is more easily triggered, but the percentage increase in precipitation is similar for different areas. There are substantial regional differences in the patterns of irrigation impact, but, for all the studied regions, the highest percentage contribution to precipitation is over local land.

Intensity of climate variability derived from the satellite and MERRA reanalysis temperatures: AO, ENSO, and QBO

Satellite measurements (Atmospheric InfraRed Sounder/Advanced Microwave Sounding Unit-A, MODerate resolution Imaging Spectroradiometer) and the Modern Era Retrospective-analysis for Research and Applications (MERRA) reanalysis have been utilized to analyze the relative influence of the climate variability (AO: Arctic Oscillation, ENSO: El Niño-Southern Oscillation, QBO: Quasi-Biennial Oscillation) on the zonal-mean temperature and wind variations over the globe from September 2002 to August 2011. We also extended the usage of MERRA data for the period of 1979–2011; furthermore, three climate indices of AO, NINO3.4, and QBO were used as the corresponding climate indicators. The correlations between the temperature anomalies and the climate indices indicate that the tropospheric temperature variability in the mid-latitude (30–60N) linked to both AO and ENSO has been more pronounced over ocean than over land. However, the low stratospheric temperature variability in the mid-latitude is mainly associated with ENSO and QBO. The north–south symmetric patterns over the globe are seen in the wind anomaly distributions for ENSO and QBO, but not for AO. The ENSO events are globally vigorous but also localized during the recent 9 years compared with those based on the period of 1979–2011. The tropospheric warming and stratospheric cooling phenomena during this period are more remarkable in the recent 9 years, although according to IPCC (2012). their linkage to the ENSO variability is still uncertain. The ENSO is found to have more significant impact on the tropospheric and low stratosphere temperature variability over the tropics in the recent period, consistent with more active zonal wind meridional circulations. The discrepancies between satellite observations and MERRA are also discussed. The estimated relative impact of the three major concurrent large-scale climate phenomena on regional temperature variability can be of great use in its long-term predictability.

Evaluating CMIP5 models using AIRS tropospheric air temperature and specific humidity climatology

This paper documents the climatological mean features of the Atmospheric Infrared Sounder (AIRS) monthly mean tropospheric air temperature (ta, K) and specific humidity (hus, kg/kg) products as part of the Obs4MIPs project and compares them to those from NASA's Modern Era Retrospective analysis for Research and Applications (MERRA) for validation and 16 models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) for CMIP5 model evaluation. MERRA is warmer than AIRS in the free troposphere but colder in the boundary layer with differences typically less than 1 K. MERRA is also drier (~10%) than AIRS in the tropical boundary layer but wetter (~30%) in the tropical free troposphere and the extratropical troposphere. In particular, the large MERRA‐AIRS specific humidity differences are mainly located in the deep convective cloudy regions indicating that the low sampling of AIRS in the cloudy regions may be the main reason for these differences. In comparison to AIRS and MERRA, the sixteen CMIP5 models can generally reproduce the climatological features of tropospheric air temperature and specific humidity well, but several noticeable biases exist. The models have a tropospheric cold bias (around 2 K), especially in the extratropical upper troposphere, and a double‐ITCZ problem in the troposphere from 1000 hPa to 300 hPa, especially in the tropical Pacific. The upper‐tropospheric cold bias exists in the most (13 of 16) models, and the double‐ITCZ bias is found in all 16 CMIP5 models. Both biases are independent of the reference dataset used (AIRS or MERRA).

Comparisons of Clear-Sky Outgoing Far-IR Flux Inferred from Satellite Observations and Computed from the Three Most Recent Reanalysis Products

Abstract The far-IR spectrum plays an important role in the earth’s radiation budget and remote sensing. The authors compare the near-global (80°S–80°N) outgoing clear-sky far-IR flux inferred from the collocated Atmospheric Infrared Sounder (AIRS) and Clouds and the Earth’s Radiant Energy System (CERES) observations in 2004 with the counterparts computed from reanalysis datasets subsampled along the same satellite trajectories. The three most recent reanalyses are examined: the ECMWF Interim Re-Analysis (ERA-Interim), NASA Modern-Era Retrospective Analysis for Research and Application (MERRA), and NOAA/NCEP Climate Forecast System Reanalysis (CFSR). Following a previous study by X. Huang et al., clear-sky spectral angular distribution models (ADMs) are developed for five of the CERES land surface scene types as well as for the extratropical oceans. The outgoing longwave radiation (OLR) directly estimated from the AIRS radiances using the authors’ algorithm agrees well with the OLR in the collocated CERES Single Satellite Footprint (SSF) dataset. The daytime difference is 0.96 ±2.02 W m−2, and the nighttime difference is 0.86 ±1.61 W m−2. To a large extent, the far-IR flux derived in this way agrees with those directly computed from three reanalyses. The near-global averaged differences between reanalyses and observations tend to be slightly positive (0.66%–1.15%) over 0–400 cm−1 and slightly negative (−0.89% to −0.44%) over 400–600 cm−1. For all three reanalyses, the spatial distributions of such differences show the largest discrepancies over the high-elevation areas during the daytime but not during the nighttime, suggesting discrepancies in the diurnal variation of such areas among different datasets. The composite differences with respect to temperature or precipitable water suggest large discrepancies for cold and humid scenes.

Long-term statistical assessment of Aqua-MODIS aerosol optical depth over coastal regions: bias characteristics and uncertainty sources

Coastal regions around the globe represent a major source for anthropogenic aerosols in the atmosphere, but the surface characteristics may not be optimal for the Moderate Resolution Imaging Spectroradiometer (MODIS) algorithms designed for aerosol retrievals over dark land or ocean surfaces. Using data collected from 62 coastal stations worldwide by the Aerosol Robotic Network (AERONET) in 2002–2011, statistical assessments of uncertainties are conducted for coastal aerosol optical depth (AOD) retrieved from MODIS measurements aboard the Aqua satellite (i.e., the Collection 5.1 MYD04 data product generated by the MODIS atmosphere group). It is found that coastal AODs (at 550 nm) characterised respectively by the Dark Land algorithm and the Dark Ocean algorithm all exhibit a log-normal distribution, which contrasts to the near-normal distribution of their corresponding biases. After data filtering using quality flags, the MODIS AODs from both the Dark Land and Dark Ocean algorithms over coastal regions are highly correlated with AERONET AODs (R2≈0.8), but both have larger uncertainties than their counterparts (of MODIS AODs) over land and open ocean. Overall, the Dark Ocean algorithm overestimates the AERONET coastal AOD by 0.021 for AOD < 0.25 and underestimates it by 0.029 for AOD > 0.25. This dichotomy is shown to be related to the ocean-surface wind speed and cloud-contamination effects on the MODIS aerosol retrievals. Consequently, an empirical correction scheme is formulated that uses cloud fraction and sea-surface wind speed from Modern Era Retrospective-Analysis for Research and Applications (MERRA) to correct the AOD bias from the Dark Ocean algorithm, and it is shown to be effective over the majority of the coastal AERONET stations to (a) simultaneously reduce both the mean and the spread of the bias and (b) improve the trend analysis of AOD. Further correlation analysis performed after such an empirical bias correction shows that the MODIS AOD is also likely impacted by the concentration of suspended particulate matter in coastal waters, which is not taken into account during the MODIS AOD retrievals. While mathematically the MODIS AODs over the global coastal AERONET sites show statistically significant discrepancies (p<1%) from their respective AERONET-measured counterparts in terms of mean and frequency, different applications of MODIS AODs in climate and air-quality studies often have their own tolerances of uncertainties. Nevertheless, it is recommended that an improved treatment of varying sea-surface wind and sediment over coastal waters be an integral part in the continuous evolution of the MODIS AOD retrieval algorithms.

A Characterization of the Variation in Relative Humidity across West Africa during the Dry Season

AbstractThe variation of relative humidity across West Africa during the dry season is evaluated using the Modern Era Retrospective Analysis for Research and Applications (MERRA) dataset and the method of self-organizing maps. Interest in the dry season of West Africa is related to the connection between near-surface atmospheric moisture and the occurrence of meningitis across West Africa, most notably in the region known as the meningitis belt. The patterns in relative humidity are analyzed in terms of frequency of each pattern as well as the sequencing from one pattern to the next. The variations in relative humidity are characterized subannually for individual years from 1979 to 2009 as well as decadally over the entire 30-yr duration of dry seasons in West Africa. The progression from relatively moist patterns to relatively dry patterns and back to the moist patterns over the course of the dry season corresponds to the northward and then southward migration of the intertropical convergence zone. The results indicate distinctly different frequency and sequencing of relative humidity patterns from year to year. The year-to-year changes in relative humidity patterns are gradual. There is some indication of a larger, possibly decadal, pattern to the year-to-year changes in the variation of relative humidity over the course of the dry season. The results are reflective of the reanalysis data including potentially unusual and erroneously dry conditions in central Africa after the mid-1990s.

Estimation of evapotranspiration over the terrestrial ecosystems in China

ABSTRACTQuantifying regional evapotranspiration (ET) and environmental constraints are particularly important for understanding water and carbon cycles of terrestrial ecosystems. However, a large uncertainty in the regional estimation of ET still remains for the terrestrial ecosystems in China. This study used ET measurements of 34 eddy covariance sites within China and adjacent regions to examine the performance of the revised Remote Sensing‐Penman Monteith (RS‐PM) model over various ecosystem types including forests, grasslands, wetlands and croplands. No significant systematic error was found in the revised RS‐PM model predictions, which explained 61% of the ET variations at all of the validation sites. Regional patterns of ET at a spatial resolution of 10 × 10 km were quantified using a meteorology dataset from 753 meteorological stations, Modern Era Retrospective‐analysis for Research and Applications (MERRA) reanalysis products and satellite data such as the Advanced Very High Resolution Radiometer (AVHRR) leaf area index. ET decreased from the southeast of China toward the northwest. Relatively high ET values were found in the southern China such as Yunnan, Hainan, Fujian and Guangdong Provinces, whereas low ET values occurred in northwestern China such as in the Xinjiang autonomous region. On average, the annual ET presented an increasing trend during the 1982–2009, with relatively low ET in 1985, 1993, 1997, 2000 and 2009. We found that the mean annual ET was higher than world average, ranging spatially between 484 and 521 mm yr−1, with a mean value of 500 mm yr−1, which accounted for approximately 5·6–8·3% of the world's total land‐surface ET. Copyright © 2012 John Wiley &amp; Sons, Ltd.

Analysis of large‐amplitude stratospheric mountain wave event observed from the AIRS and MLS sounders over the western Himalayan region

Mountain waves in the stratosphere have been observed over elevated topographies using both nadir‐looking and limb‐viewing satellites. However, the characteristics of mountain waves generated over the Himalayan Mountain range and the adjacent Tibetan Plateau are relatively less explored. The present study reports on three‐dimensional (3‐D) properties of a mountain wave event that occurred over the western Himalayan region on 9 December 2008. Observations made by the Atmospheric Infrared Sounder on board the Aqua and Microwave Limb Sounder on board the Aura satellites are used to delineate the wave properties. The observed wave properties such as horizontal (λx, λy) and vertical (λz) wavelengths are 276 km (zonal), 289 km (meridional), and 25 km, respectively. A good agreement is found between the observed and modeled/analyzed vertical wavelength for a stationary gravity wave determined using the Modern Era Retrospective Analysis for Research and Applications (MERRA) reanalysis winds. The analysis of both the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis and MERRA winds shows that the waves are primarily forced by strong flow across the topography. Using the 3‐D properties of waves and the corrected temperature amplitudes, we estimated wave momentum fluxes of the order of ∼0.05 Pa, which is in agreement with large‐amplitude mountain wave events reported elsewhere. In this regard, the present study is considered to be very much informative to the gravity wave drag schemes employed in current general circulation models for this region.

Object-Based Evaluation of MERRA Cloud Physical Properties and Radiative Fluxes during the 1998 El Niño–La Niña Transition

The Modern-Era Retrospective Analysis for Research and Application (MERRA) is a reanalysis designed to produce an improved representation of the Earth’s hydrologic cycle. This study examines the representation of deep convective clouds in MERRA, comparing analyzed liquid and ice clouds with deep convective cloud objects observed by instruments on the Tropical Rainfall Measuring Mission satellite. Results show that MERRA contains deep convective cloud in 98.1% of the observed cases. MERRA-derived probability density functions (PDFs) of cloud properties have a similar form as the observed PDFs and exhibit a similar trend with changes in object size. Total water path, optical depth, and outgoing shortwave radiation (OSR) in MERRA are found to match the cloud object observations quite well; however, there appears to be a bias toward higher-than-observed cloud tops in the MERRA. The reanalysis fits the observations most closely for the largest class of convective systems, with performance generally decreasing with a transition to smaller convective systems. Comparisons of simulated total water path, optical depth, and OSR are found to be highly sensitive to the assumed subgrid distribution of condensate and indicate the need for caution when interpreting model-data comparisons that require disaggregation of grid-scale cloud to satellite pixel scales.