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CGD Research Catalog
Dr. Aiguo Dai
Abstract: The hydrometeorology of the Amazon basin in the ERA-40 reanalysis for 1958–2001 is compared with observations of precipitation, temperature, and streamflow. After 1979, the reanalysis over the Amazon has a small cool bias of the order of _0.35 K, and a small low bias of precipitation of the order of _0.3 mm day_1. In the early years (1958–72), there is a large upward drift in reanalysis precipitation and runoff associated with an upward drift in the atmospheric water vapor in the analysis, and a somewhat smaller downward drift of temperature as precipitation increases. In the presatellite data, there are inhomogeneities in the radiosonde and surface synoptic data, and there were problems with the variational analysis of humidity once satellite radiances were introduced. Approximate bias corrections can be made for precipitation and runoff on an annual basis, but this also removes some of the interannual variability. The reanalysis runoff–precipitation relationship is similar to the observed streamflow–precipitation relation, on an annual water-year basis. Compared to observations, ERA-40 precipitation for the Amazon is low by about 1.3 mm day_1 in the rainy season, and high by a smaller amount in the dry season. The precipitation bias produces a temperature bias in ERA-40 of the opposite sign on the annual time scale. The reanalysis has a small cold temperature bias after 1967, but on an annual time scale it reproduces the interannual variability of the observations. Although the biases in temperature and precipitation in recent decades are small, the difficulties with the analysis of atmospheric water vapor lead to large uncertainty in long-term trends of the water cycle. This work was partly supported by the NCAR Water Cycle Across Scales Initiative and NSF. Figure. (High resolution figure.)
Abstract: In situ observations of surface air and dewpoint temperatures and air pressure from over 15 000 weather stations and from ships are used to calculate surface specific (q) and relative (RH) humidity over the globe 60°S–75°N) from December 1975 to spring 2005. Seasonal and interannual variations and linear trends are analyzed in relation to observed surface temperature (T) changes and simulated changes by a coupled climate model [namely the Parallel Climate Model (PCM)] with realistic forcing. It is found that spatial patterns of long-term mean q are largely controlled by climatological surface temperature, with the largest q of 17–19 g kg_1 in the Tropics and large seasonal variations over northern mid- and high-latitude land. Surface RH has relatively small spatial and interannual variations, with a mean value of 75%–80% over most oceans in all seasons and 70%–80% over most land areas except for deserts and high terrain, where RH is 30%–60%. Nighttime mean RH is 2%–15% higher than daytime RH over most land areas because of large diurnal temperature variations. The leading EOFs in both q and RH depict long-term trends, while the second EOF of q is related to the El Niño–Southern Oscillation (ENSO). During 1976–2004, global changes in surface RH are small (within 0.6% for absolute values), although decreasing trends of_0.11%__0.22% decade_1 for global oceans are statistically significant. Large RH increases (0.5%–2.0% decade_1) occurred over the central and eastern United States, India, and western China, resulting from large q increases coupled with moderate warming and increases in low clouds over these regions during 1976–2004. Statistically very significant increasing trends are found in global and Northern Hemispheric q and T. From 1976 to 2004, annual q (T) increased by 0.06 g kg_1 (0.16°C) decade_1 globally and 0.08 g kg_1 (0.20°C) decade_1 in the Northern Hemisphere, while the Southern Hemispheric q trend is positive but statistically insignificant. Over land, the q and T trends are larger at night than during the day. The largest percentage increases in surface q (_1.5% to 6.0% decade_1) occurred over Eurasia where large warming (_0.2° to 0.7°C decade_1) was observed. The q and T trends are found in all seasons over much of Eurasia (largest in boreal winter) and the Atlantic Ocean. Significant correlation between annual q and T is found over most oceans (r _ 0.6–0.9) and most of Eurasia (r _ 0.4–0.8), whereas it is insignificant over subtropical land areas. RH–T correlation is weak over most of the globe but is negative over many arid areas. The q–T anomaly relationship is approximately linear so that surface q over the globe, global land, and ocean increases by _4.9%, 4.3%, and 5.7% per 1°C warming, respectively, values that are close to those suggested by the Clausius–Clapeyron equation with a constant RH. The recent q and T trends and the q–T relationship are broadly captured by the PCM; however, the model overestimates volcanic cooling and the trends in the Southern Hemisphere. Partly supported by the NCAR Water Cycle Program and the National Science Foundation. Figure: (High resolution figure.) caption
Abstract: Monthly and 3-hourly precipitation data from 20th century climate simulations by the newest generation of eighteen coupled climate system models are analyzed and compared with available observations. The characteristics examined include mean spatial patterns, intraseasonal to interannual and ENSO-related variability, convective versus stratiform precipitation ratio, precipitation frequency and intensity for different precipitation categories, and the diurnal cycle. Although most models reproduce the observed broad patterns of precipitation amount and year-to-year variability, models without flux corrections still show an unrealistic double ITCZ pattern over the tropical Pacific, whereas the flux-corrected models, especially MRI-CGCM2.3.2a, produce realistic rainfall patterns at low latitudes. As in previous generations of coupled models, the rainfall double ITCZs are related to westward expansion of the cold tongue of sea surface temperature that is observed only over the equatorial eastern Pacific but extends to the central Pacific in the models. The partitioning of the total variance of precipitation among intraseasonal, seasonal and longer time scales is generally reproduced by the models, except over the western Pacific where the models fail to capture the large intraseasonal variations. Most models produce too much convective (over 95% of total precipitation) and too little stratiform precipitation over most of the low-latitudes, in contrast to 45-65% in the TRMM satellite observations. The biases in the convective versus stratiform precipitation ratio are linked to unrealistically strong coupling of tropical convection to local sea surface temperature (SST), which results in a positive correlation between the standard deviation of Niño 3.4 SST and the local convective-to-total precipitation ratio among the models. The models reproduce the percentage contribution (to total precipitation) and frequency for moderate precipitation (10-20 mm/day), but underestimate the contribution and frequency for heavy (>20 mm/day) precipitation and overestimate them for light (10 mm/day) precipitation. The newest generation of coupled models still rains too frequently, mostly within the 1-10 mm/day category. Precipitation intensity over the storm tracks around the eastern coasts of Asia and North America has intensity comparable to that in the ITCZ (10-12 mm/day) in the TRMM data, but it is much weaker in the models. The diurnal analysis suggests that warm-season convection still starts too early in the new models, and occurs too frequently at reduced intensity in some of the models. The results show that considerable improvements in precipitation simulations are still desirable for the latest generation of world's coupled climate models. Supported by NSF SGER Grant #ATM-0451587 and the NCAR Water Cycle Program. Figure: (High resolution figure.) caption
Abstract: A 1200-yr unforced control run and future climate change simulations using the Parallel Climate Model (PCM), a coupled atmosphere–ocean–land–sea ice global model with no flux adjustments and relatively high resolution (~2.8° for the atmosphere and 2/3° for the oceans) are analyzed for changes in Atlantic Ocean circulations. For the forced simulations, historical greenhouse gas and sulfate forcing of the twentieth century and projected forcing for the next two centuries are used. The Atlantic thermohaline circulation (THC) shows large multidecadal (15–40 yr) variations with mean-peak amplitudes of 1.5–3.0 Sv (1 Sv = 106 m3 s-1) and a sharp peak of power around a 24-yr period in the control run. Associated with the THC oscillations, there are large variations in North Atlantic Ocean heat transport, sea surface temperature (SST) and salinity (SSS), sea ice fraction, and net surface water and energy fluxes, which all lag the variations in THC strength by 2–3 yr. However, the net effect of the SST and SSS variations on upper-ocean density in the midlatitude North Atlantic leads the THC variations by about 6 yr, which results in the 24-yr period. The simulated SST and sea ice spatial patterns associated with the THC oscillations resemble those in observed SST and sea ice concentrations that are associated with the North Atlantic Oscillation (NAO). The results suggest a dominant role of the advective mechanism and strong coupling between the THC and the NAO, whose index also shows a sharp peak around the 24-yr time scale in the control run. In the forced simulations, the THC weakens by ~12% in the twenty-first century and continues to weaken by an additional ~10% in the twenty-second century if CO2 keeps rising, but the THC stabilizes if CO2 levels off. The THC weakening results from stabilizing temperature increases that are larger in the upper and northern Atlantic Ocean than in the deep and southern parts of the basin. In both the control and forced simulations, as the THC gains (loses) strength and depth, the separated Gulf Stream (GS) moves southward (northward) while the subpolar gyre centered at the Labrador Sea contracts from (expands to) the east with the North Atlantic Current (NAC) being shifted westward (eastward). These horizontal circulation changes, which are dynamically linked to the THC changes, induce large temperature and salinity variations around the GS and NAC paths. Supported by the Office of Biological and Environmental Research, U.S. Department of Energy, and U.S. National Science Foundation. Figure. (High resolution figure.) caption
Abstract: Automated surface observation systems (ASOS) were widely introduced to replace manned weather stations around the middle 1990s over North America and other parts of the world. While laser beam ceilometers of the ASOS in North America measure overhead clouds within the lower 3.8 km of the atmosphere, they do not contain cloud type and opacity information and are not comparable with previous cloud records. However, a network of 124 U.S. military weather stations with continuous human observations provides useful information of total cloud cover averaged over the contiguous United States, thus partially lessening the disruption caused by the ASOS. Analyses of the military cloud data suggest an increasing trend (~1.4% of the sky covered per decade) in U.S. total cloud cover from 1976 to 2004, with increases over most of the country except the Northwest. Nevertheless, inadequacies exist in surface observations of global cloud amounts and types, especially over the oceans and over Canada and the United States since the middle 1990s. The problem is compounded by inhomogeneities in satellite cloud data. Reprocessing of satellite data has potential for improvements if priority is given to improved continuity of records. This work was partly supported by the NCAR Water Cycle Program. Figure. (High resolution figure.) caption
Abstract: A mesoscale model (MM5)–based regional climate model (CMM5) integration driven by the Parallel Climate Model (PCM), a fully coupled atmosphere-ocean-land-ice general circulation model (GCM), for the present (1986–1995) summer season climate is first compared with observations to study the CMM5's downscaling skill and uncertainty over the United States. The results indicate that the CMM5, with its finer resolution (30 km) and more comprehensive physics, simulates the present U.S. climate more accurately than the driving PCM, especially for precipitation, including summer mean patterns, diurnal cycles, and daily frequency distributions. Hence the CMM5 downscaling provides a credible means to improve GCM climate simulations. A parallel CMM5 integration driven by the PCM future (2041–2050) projection is then analyzed to determine the downscaling impact on regional climate changes. It is shown that the CMM5 generates climate change patterns very different from those predicted by the driving PCM. A key difference is a summer “warming hole” over the central United States in the CMM5 relative to the PCM. This study shows that the CMM5 downscaling can significantly reduce GCM biases in simulating the present climate and that this improvement has important consequences for future projections of regional climate changes. For both the present and future climate simulations, the CMM5 results are sensitive to the cumulus parameterization, with strong regional dependence. The deficiency in representing convection is likely the major reason for the PCM's unrealistic simulation of U.S. precipitation patterns and perhaps also for its large warming in the central United States. This work is partly supported by NCAR’s Water Cycle Program and the National Science Foundation. Figure. (High resolution figure.) caption
Abstract: Daily precipitation data from worldwide stations and gridded analyses and from 18 coupled global climate models are used to evaluate the models' performance in simulating the precipitation frequency, intensity, and the number of rainy days contributing to most (i.e., 67%) of the annual precipitation total. Although the models examined here are able to simulate the land precipitation amount well, most of them are unable to reproduce the spatial patterns of the precipitation frequency and intensity. For light precipitation (1–10 mm day-1), most models overestimate the frequency but produce patterns of the intensity that are in broad agreement with observations. In contrast, for heavy precipitation (>10 mm day-1), most models considerably underestimate the intensity but simulate the frequency relatively well. The average number of rainy days contributing to most of the annual precipitation is a simple index that captures the combined effects of precipitation frequency and intensity on the water supply. The different measures of precipitation characteristics examined in this paper reveal region-to-region differences in the observations and models of relevance for climate variability, water resources, and climate change. Partially supported by the NCAR Water Cycle Across-Scales Initiative. NSF. Figure. (High resolution figure.) caption
Abstract: Water-vapor-weighted atmospheric mean temperature, Tm, is a key parameter in the retrieval of atmospheric precipitable water (PW) from ground-based Global Positioning System (GPS) measurements of zenith path delay (ZPD), as the accuracy of the GPS-derived PW is proportional to the accuracy of Tm. We compare and analyze global estimates of Tm from three different data sets from 1997 to 2002: the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-year reanalysis (ERA-40), the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis, and the newly released Integrated Global Radiosonde Archive (IGRA) data set. Temperature and humidity profiles from both the ERA-40 and NCEP/NCAR reanalyses produce reasonable Tm estimates compared with those from the IGRA soundings. The ERA-40, however, is a better option for global Tm estimation because of its better performance and its higher spatial resolution. Tm is found to increase from below 255 K in polar regions to 295–300 K in the tropics, with small longitudinal variations. Tm has an annual range of ~2–4 K in the tropics and 20–35 K over much of Eurasia and northern North America. The day-to-day Tm variations are 1–3 K over most low latitudes and 4–7 K (2–4 K) in winter (summer) Northern Hemispheric land areas. Diurnal variations of Tm are generally small, with mean-to-peak amplitudes less than 0.5 K over most oceans and 0.5–1.5 K over most land areas and a local time of maximum around 16–20 LST. The commonly used Tm-Ts relationship from Bevis et al. (1992) is evaluated using the ERA-40 data. Tm derived from this relationship (referred to as Tmb) has a cold bias in the tropics and subtropics (-1 ~ -6 K, largest in marine stratiform cloud regions) and a warm bias in the middle and high latitudes (2–5 K, largest over mountain regions). The random error in Tmb is much smaller than the bias. A serious problem in Tmb is its erroneous large diurnal cycle owing to diurnally invariant Tm-Ts relationship and large Ts diurnal variations, which could result in a spurious diurnal cycle in GPS-derived PW and cause 1–2% day-night biases in GPS-based PW. This work is supported by NCAR Director Office's Opportunity Fund and partially by NCAR Water Cycle Across-Scale Initiative. The National Center for Atmospheric Research is sponsored by the National Science Foundation. Figure. (High resolution figure.) caption |
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Betts, A. K., J. H. Ball,
P. Viterbo, A. Dai, and J. A. Marengo, 2005: Hydrometeorology of the Amazon
in ERA-40. Journal of Hydrometeorology, 6, 764-774.
Dai, A., 2006: Recent
climatology, variability and trends in global surface humidity. Journal
of Climate, 19, 3589-3606.
Dai, A., 2006: Precipitation
characteristics in eighteen coupled climate models. J. Climate, 19, 4605-4630.
Dai, A., A. Hu, G. A.
Meehl, W. M. Washington, and W. G. Strand, 2005: Atlantic thermohaline
circulation in a coupled model: Unforced variations vs. forced changes.
Journal of Climate, 18, 3270-3293.
Dai, A., T. R. Karl,
B. Sun, and K. E. Trenberth, 2006: Recent trends in cloudiness over the
United States. Bulletin of American Meteorological Society, 87, 597-606.
Liang, X.-Z., J. Pan,
J. Zhu, K. E. Kunkel, J. X. L. Wang, and A. Dai, 2006: Regional climate
model downscaling of the U.S. summer climate and future change. Journal
of Geophysical Res., 111, D10108, doi: 10.1029/2005JD006685.
Sun, Y., S. Solomon,
A. Dai, and R. W. Portmann, 2006: How often does it rain. Journal of Climate,
19, 916-934.
Wang, J., L. Zhang, and
A. Dai, 2005: Global estimates of water-vapor-weighted mean temperature
of the atmosphere for GPS applications. Journal of Geophysical Research,
110, D21101, 10.1029/2005JD006215.
