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Aiguo Dai

Abstract

Present and past weather reports from ∼15 000 stations around the globe and from the Comprehensive Ocean–Atmosphere Data Set from 1975 to 1997 were analyzed for the frequency of occurrence for and the percentage of the days with various types of precipitation (drizzle, nondrizzle, showery, nonshowery, and snow) and thunderstorms. In this paper, the mean geographical, seasonal, and interannual variations in the frequencies are documented. Drizzles occur most frequently (∼5%–15% of the time) over mid- and high-latitude oceans. Nonshowery precipitation is the preferred form of precipitation over the storm-track regions at northern mid- and high latitudes in boreal winter and over the Antarctic Circumpolar Current in all seasons. Showery precipitation occurs ∼5%–20% of the time over the oceans, as compared with < 10% over land areas except in boreal summer over Northern Hemisphere land areas, where showery precipitation and thunderstorms occur in over 20% of the days. Inferred mean precipitation intensity is generally < 1.0 mm h−1 at mid- and high latitudes and ∼1.5–3.0 mm h−1 in the Tropics. The intertropical convergence zone and the South Pacific convergence zone are clearly defined in the frequency maps but not in the intensity maps. Nonshowery precipitation at low latitudes is associated with showery precipitation, consistent with observations of stratiform precipitation accompanying mesoscale convective systems in the Tropics. The seasonal cycles of the showery precipitation and thunderstorm frequencies exhibit a coherent land–ocean pattern in that land areas peak in summer and the oceans peak in winter. The leading EOFs in the nondrizzle and nonshowery precipitation frequencies are an ENSO-related mode that confirms the ENSO-induced precipitation anomalies over the open oceans previously derived from satellite estimates.

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Aiguo Dai

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 qT 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 qT relationship are broadly captured by the PCM; however, the model overestimates volcanic cooling and the trends in the Southern Hemisphere.

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Aiguo Dai

Abstract

Monthly and 3-hourly precipitation data from twentieth-century climate simulations by the newest generation of 18 coupled climate system models are analyzed and compared with available observations. The characteristics examined include the mean spatial patterns, intraseasonal-to-interannual and ENSO-related variability, convective versus stratiform precipitation ratio, precipitation frequency and intensity for different precipitation categories, and 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 the Meteorological Research Institute (MRI) Coupled Global Climate Model (CGCM; version 2.3.2a), 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 (SST) 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 convective form in the Tropical Rainfall Measuring Mission (TRMM) satellite observations. The biases in the convective versus stratiform precipitation ratio are linked to the unrealistically strong coupling of tropical convection to local 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 of the contribution (to total precipitation) and frequency for moderate precipitation (10–20 mm day−1), but underestimate the contribution and frequency for heavy (>20 mm day−1) and overestimate them for light (<10 mm day−1) precipitation. The newest generation of coupled models still rains too frequently, mostly within the 1–10 mm day−1 category. Precipitation intensity over the storm tracks around the eastern coasts of Asia and North America is comparable to that in the ITCZ (10–12 mm day−1) 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 these 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 the world’s coupled climate models.

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Aiguo Dai

Abstract

Three-hourly present weather reports from ∼15 000 stations around the globe and from the Comprehensive Ocean–Atmosphere Data Set from 1975 to 1997 were analyzed for diurnal variations in the frequency of occurrence for various types of precipitation (drizzle, nondrizzle, showery, nonshowery, and snow) and thunderstorms. Significant diurnal variations with amplitudes exceeding 20% of the daily mean are found over much of the globe, especially over land areas and during summer. Drizzle and nonshowery precipitation occur most frequently in the morning around 0600 local solar time (LST) over most land areas and from midnight to 0400 LST over many oceanic areas. Showery precipitation and thunderstorms occur much more frequently in the late afternoon than other times over most land areas in all seasons, with a diurnal amplitude exceeding 50% of the daily mean frequencies. Over the North Pacific, the North Atlantic, and many other oceanic areas adjacent to continents, showery precipitation is most frequent in the morning around 0600 LST, which is out of phase with land areas. Over the tropical and southern oceans, showery precipitation tends to peak from midnight to 0400 LST. Maritime thunderstorms occur most frequently around midnight. It is suggested that the diurnal variations in atmospheric relative humidity contribute to the morning maximum in the frequency of occurrence for drizzle and nonshowery precipitation, especially over land areas. Solar heating on the ground produces a late-afternoon maximum of convective available potential energy in the atmosphere that favors late-afternoon moist convection and showery precipitation over land areas during summer. This strong continental diurnal cycle induces a diurnal cycle of opposite phase in low-level convergence over large nearby oceanic areas that favors a morning maximum of maritime showery precipitation. Larger low-level convergence induced by pressure tides and higher relative humidity at night than at other times may contribute to the nighttime maximum of maritime showery and nonshowery precipitation over remote oceans far away from continents.

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Tianbao Zhao and Aiguo Dai

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Atmospheric demand for moisture and dry days are expected to increase, leading to drying over land in the twenty-first century. Here, the magnitude and key drivers of this drying are investigated using model simulations under a low–moderate scenario, RCP4.5. The self-calibrated Palmer drought severity index with the Penman–Monteith potential evapotranspiration (PET) (sc_PDSI_pm), top 10-cm soil moisture (SM), and runoff (R) from 14 models are analyzed. The change patterns are found to be comparable while the magnitude differs among these measures of drought. The frequency of the SM-based moderate (severe) agricultural drought could increase by 50%–100% (100%–200%) in a relative sense by the 2090s over most of the Americas, Europe, and southern Africa and parts of East and West Asia and Australia. Runoff-based hydrological drought frequency could also increase by 10%–50% over most land areas despite increases in mean runoff. The probability density functions (PDFs) flatten, enhancing the drought increases induced primarily by decreases in the mean. Precipitation (P) and evapotranspiration (E) changes contribute to the SM change; whereas decreases in sc_PDSI_pm result from ubiquitous PET increases of 10%–20% with contributions from decreased P over subtropical areas. Rising temperatures and vapor deficits explain most of the PET increase, which in turn explains most of the E increases over Asia and northern North America while decreased SM leads to lower E over the rest of the world. Radiation and wind speed changes have only small effects on future PET and drought. Globally, runoff ratio changes little while P, E, and R all increase by about 4%–5% in the twenty-first century.

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Aiguo Dai and Jiechun Deng

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Arctic amplification (AA) reduces meridional temperature gradients (dT/dy) over the northern mid-high latitudes, which may weaken westerly winds. It is suggested that this may lead to wavier and more extreme weather in the midlatitudes. However, temperature variability is shown to decrease over the northern mid-high latitudes under increasing greenhouse gases due to reduced dT/dy. Here, through analyses of coupled model simulations and ERA5 reanalysis, it is shown that consistent with previous studies, cold-season surface and lower-mid troposphere temperature (T) variability decreases over northern mid-high latitudes even in simulations with suppressed AA and sea ice loss under increasing CO2; however, AA and sea ice loss further reduce the T variability greatly, leading to a narrower probability distribution and weaker cold or warm extreme events relative to future mean climate. Increased CO2 strengthens meridional wind (υ) with a wavenumber-4 pattern but weakens meridional thermal advection [−υ(dT/dy)] over most northern mid-high latitudes, and AA weakens the climatological υ and −υ(dT/dy). The weakened thermal advection and its decreased variance are the primary causes of the T variability decrease, which is enlarged by a positive feedback between the variability of T and −υ(dT/dy). AA not only reduces dT/dy, but also its variance, which further decreases T variability through −υ(dT/dy). While the mean snow and ice cover decreases, its variability increases over many northern latitudes, and these changes do not weaken the T variability. Thus, AA’s influence on midlatitude temperature variability comes mainly from its impact on thermal advection, rather than on winds as previously thought.

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Aiguo Dai and Junhong Wang

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Global surface pressure data from 1976 to 1997 from over 7500 land stations and the Comprehensive Ocean–Atmosphere Data Set have been analyzed using harmonic and zonal harmonic methods. It is found that the diurnal pressure oscillation (S 1) is comparable to the semidiurnal pressure oscillation (S 2) in magnitude over much of the globe except for the low-latitude open oceans, where S 2 is about twice as strong as S 1. Over many land areas, such as the western United States, the Tibetan Plateau, and eastern Africa, S 1 is even stronger than S 2. This is in contrast to the conventional notion that S 2 predominates over much of the globe. The highest amplitudes (∼1.3 mb) of S 1 are found over northern South America and eastern Africa close to the equator. Here S 1 is also strong (∼1.1 mb) over high terrain such as the Rockies and the Tibetan Plateau. The largest amplitudes of S 2 (∼1.0–1.3 mb) are in the Tropics over South America, the eastern and western Pacific, and the Indian Ocean. Here S 1 peaks around 0600–0800 LST at low latitudes and around 1000–1200 LST over most of midlatitudes, while S 2 peaks around 1000 and 2200 LST over low- and midlatitudes. Here S 1 is much stronger over the land than over the ocean and its amplitude distribution is strongly influenced by landmasses, while the land–sea differences of S 2 are small. The spatial variations of S 1 correlate significantly with spatial variations in the diurnal temperature range at the surface, suggesting that sensible heating from the ground is a major forcing for S 1. Although S 2 is much more homogeneous zonally than S 1, there are considerable zonal variations in the amplitude of S 2, which cannot be explained by zonal variations in ozone and water vapor. Other forcings such as those through clouds’ reflection and absorption of solar radiation and latent heating in convective precipitation are needed to explain the observed regional and zonal variations in S 2. The migrating tides S11 and S22 predominate over other zonal wave components. However, the nonmigrating tides are substantially stronger than previously reported. The amplitudes of both the migrating and nonmigrating tides decrease rapidly poleward with a slower pace at middle and high latitudes.

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Adam Hugh Monahan and Aiguo Dai

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The spatial structure of asymmetries in sea surface temperature (SST) and surface air temperature (SAT) between average El Niño and La Niña events is considered. It is demonstrated that in historical SST and SAT reconstructions, the anomaly spatial pattern that changes sign between El Niño and La Niña events (the “linear” signal) strongly resembles that of principal component analysis (PCA) mode 1, while that which does not change sign (the “nonlinear” signal) resembles the pattern of PCA mode 2. The linear and nonlinear patterns also strongly resemble the standard deviation and skewness fields, respectively. Furthermore, temporal subsampling of long (130 yr) SST reconstructions suggests that the magnitude of the nonlinear signal and its similarity to PCA mode 2 are functions of the strength of ENSO, as measured by the standard deviation of the PCA mode-1 time series. Finally, it is found that of several coupled general circulation models (GCMs) considered, the spatial and temporal structure of the El Niño–La Niña asymmetry is captured only by the GFDL R30 model, despite large biases in its covariance structure.

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Aiguo Dai and Kevin E. Trenberth

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Annual and monthly mean values of continental freshwater discharge into the oceans are estimated at 1° resolution using several methods. The most accurate estimate is based on streamflow data from the world's largest 921 rivers, supplemented with estimates of discharge from unmonitored areas based on the ratios of runoff and drainage area between the unmonitored and monitored regions. Simulations using a river transport model (RTM) forced by a runoff field were used to derive the river mouth outflow from the farthest downstream gauge records. Separate estimates are also made using RTM simulations forced by three different runoff fields: 1) based on observed streamflow and a water balance model, and from estimates of precipitation P minus evaporation E computed as residuals from the atmospheric moisture budget using atmospheric reanalyses from 2) the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) and 3) the European Centre for Medium-Range Weather Forecasts (ECMWF). Compared with previous estimates, improvements are made in extending observed discharge downstream to the river mouth, in accounting for the unmonitored streamflow, in discharging runoff at correct locations, and in providing an annual cycle of continental discharge. The use of river mouth outflow increases the global continental discharge by ∼19% compared with unadjusted streamflow from the farthest downstream stations. The river-based estimate of global continental discharge presented here is 37 288 ± 662 km3 yr−1, which is ∼7.6% of global P or 35% of terrestrial P. While this number is comparable to earlier estimates, its partitioning into individual oceans and its latitudinal distribution differ from earlier studies. The peak discharges into the Arctic, the Pacific, and global oceans occur in June, versus May for the Atlantic and August for the Indian Oceans. Snow accumulation and melt are shown to have large effects on the annual cycle of discharge into all ocean basins except for the Indian Ocean and the Mediterranean and Black Seas. The discharge and its latitudinal distribution implied by the observation-based runoff and the ECMWF reanalysis-based PE agree well with the river-based estimates, whereas the discharge implied by the NCEP–NCAR reanalysis-based PE has a negative bias.

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Aiguo Dai and Kevin E. Trenberth

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To evaluate the performance of version 2 of the Community Climate System Model (CCSM2) in simulating the diurnal cycle and to diagnose the deficiencies in underlying model physics, 10 years of 3-hourly data from a CCSM2 control run are analyzed for global and large-scale features of diurnal variations in surface air temperature, surface pressure, upper-air winds, cloud amount, and precipitation. The model-simulated diurnal variations are compared with available observations, most of which were derived from 3-hourly synoptic reports and some new results are reported for surface air temperatures. The CCSM2 reproduces most of the large-scale tidal variations in surface pressure and upper-air winds, although it overestimates the diurnal pressure tide by 20%–50% over low-latitude land and underestimates it over most oceans, the Rockies, and other midlatitude land areas. The CCSM2 captures the diurnal amplitude (1°–6°C) and phase [peak at 1400–1600 local solar time (LST)] of surface air temperature over land, but over ocean the amplitude is too small (≤0.2°C). The CCSM2 overestimates the mean total cloud amount by 10%–20% of the sky from ∼15°S to 15°N during both December– January–February (DJF) and June–July–August (JJA) and over northern mid- and high-latitude land areas in DJF, whereas it underestimates the cloud amount by 10%–30% in the subtropics and parts of the midlatitudes. Over the marine stratocumulus regions west to the continents, the diagnostic cloud scheme in the CCSM2 underestimates the mean stratocumulus amount by 10%–30% and does not simulate the observed large diurnal variations (∼3%–10%) in the marine stratocumulus clouds even when driven by observational data. In the CCSM2, warm-season daytime moist convection over land starts prematurely around 0800 LST, about 4 hours too early compared with observations, and plateaus from 1100 to 1800 LST, in contrast to a sharp peak around 1600–1700 LST in observations. The premature initiation of convection prevents convective available potential energy (CAPE) from accumulating in the morning and early afternoon and intense convection from occurring in the mid to late afternoon. As a result of the extended duration of daytime convection over land, the CCSM2 rains too frequently at reduced intensity despite the fairly realistic patterns of rainy days with precipitation >1 mm day−1. Furthermore, the convective versus nonconvective precipitation ratio is too high in the model as deep convection removes atmospheric moisture prematurely. The simulated diurnal cycle of precipitation is too weak over the oceans, especially for convective precipitation. These results suggest that substantial improvements are desirable in the CCSM2 in simulating cloud amount, initiation of warm-season deep convection over land, and in the diurnal cycle in sea surface temperatures.

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