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Sea surface height (SSH) variability is presented over the Black Sea during 1993–2005. The 1/4° × 1/4° resolution daily SSH fields are formed using optimal interpolation of available altimeter data. SSH variability reveals distinct maxima in the eastern and western basins, reflecting variations in the corresponding gyres. A joint examination of SSH and sea surface temperature (SST) indicates strong relationship between the two only in winter, with correlations as high as 0.6 or more. This would reflect a steric change in sea surface height due to thermal expansion averaged over a relatively deep winter mixed layer. Newly developed SSH fields also demonstrate a switch to the positive mode of SSH starting from the end of 1996 lasting ≈4 yr. Such a climatic shift is found to be strongly related to large-scale teleconnection patterns. Finally, the daily SSH and SST anomaly fields presented in this paper can supplement various applications in the Black Sea, such as examination of biological production and mesoscale eddy dynamics. [ABSTRACT FROM AUTHOR]

First-year ice has replaced multiyear ice in the Northern Sea Route area since 2008. In this area, sea ice survival during summer substantially depends on first-year ice thickness at melt onset, and thus monitoring of first-year ice thickness in the freezing period is a key to forecasting sea ice distributions in the following summer. In this paper we introduce a new algorithm to estimate flat first-year ice draft using brightness temperature data measured by the Advanced Microwave Scanning Radiometer-2 (AMSR2). The algorithm uses a gradient ratio (GR) of 18- and 36-GHz vertically polarized brightness temperatures based on decreases in sea ice emissivity in higher AMSR2 frequency channels with thermodynamic growth associated with an increase in volume scattering. Such spectral characteristics of the emissivity are examined by comparing GR values with flat first-year ice draft extracted by mode values of in situ draft data measured by a moored ice profiling sonar. The accuracy of the daily draft estimated from GR values after applying proper noise filters is about 10 cm for a draft range of 0.4–1.2 m. [ABSTRACT FROM AUTHOR]

This paper describes the repackaging and consistent distribution of the world's major atmospheric and oceanic reanalyses. It also presents examples of the usefulness of examining multiple reanalyses. This service will make it much easier for anybody using reanalysis to access multiple datasets using an approach similar to that of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Experienced users as well as students will find the standardized formatted data convenient to use. [ABSTRACT FROM AUTHOR]

Daily data from climate models submitted to the Fourth Assessment of the Intergovernmental Panel on Climate Change are compared with daily data from observations over Australia by measuring the overlap of the probability density functions (PDFs). The capacity of these models to simulate maximum temperature, minimum temperature, and precipitation is assessed. The resulting skill score is then used to exclude models with relatively poor skill region by region over Australia. The remaining sample of coupled climate models is then used to determine the seasonal changes in these three variables under a high- (A2) and low- (B1) emission scenario for 2050 and 2100. The authors demonstrate that some projected phenomena, such as the projected drying over southwest Western Australia, are robust and not caused by the inclusion of some weak models in earlier assessments. Some other results, such as the projected change in the monsoon, are more consistent among the good climate models. Consistent with earlier work, a consistent pattern of mean warming is identified in the projections. The amount of warming in the 99.7th percentile is not dramatically higher than the warming in the mean. However, while the mean warming is generally least in the south, the amount of warming in the 99.7th percentile is substantially higher along the southern coast of Australia. This is due to a coupling of the temperature response with reduced rainfall, which causes drying and allows extreme maximum temperatures to increase dramatically. The authors show that, in general, the amount of rainfall is projected to change relatively little, but the frequency of rainfall decreases and the intensity of rainfall at the upper tail of the distribution increases. However, the scale of the increase in extreme rainfall is not large on the time scales analyzed here. The range in projected temperature changes among those climate models with skill in simulating the observations is at least twice as large for the 99.7th/0.3rd percentiles as for the mean. For rainfall, the range among the good models is of order 10 times greater in the 99.7th percentile than in the mean. Since the impact of changes in extremes is increasingly recognized as societally important, this result strongly limits the use of climate model data to explore sectors that are vulnerable to extremes. This suggests an evaluation strategy that focuses on model capacity to simulate whole PDFs since capacity to simulate the mean is a necessary but insufficient criterion for determining a model’s value for future projection. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) generated a multidecadal (from 1985 to present) ensemble reforecast database for the 2012 version of the Global Ensemble Forecast System (GEFS). This dataset includes 11-member reforecasts initialized once per day at 0000 UTC. This GEFS version has a strong cold bias for winter and warm bias for summer in the Northern Hemisphere. Although the operational decaying average bias-correction approach performs well in winter and summer, it sometimes fails during the spring and fall transition seasons at long lead times (>~5 days). In this paper, 24- (1985-2008) and 25-yr (1985-2009) reforecast biases are used to calibrate 2-m temperature forecasts in 2009 and 2010, respectively. The reforecast-calibrated forecasts for both years are more accurate than those adjusted by the decaying average method during transition seasons. A long training period (>5 yr) is necessary to help avoid a large impact on bias correction from an extreme year case and keep a broader diversity of weather scenarios. The improvement from using the full 25-yr, 31-day window, weekly training dataset is almost equivalent to that from using daily training samples. This provides an option to reduce computational expenses while maintaining a desired accuracy. To provide the potential to improve forecast accuracy for transition seasons, reforecast information is added into the current operational bias-correction method. The relative contribution of the two methods is determined by the correlation between the ensemble mean and analysis. This method improves the forecast accuracy for most of the year with a maximum benefit during April-June. [ABSTRACT FROM AUTHOR]

The majority of the world's population growth to 2050 is projected to occur in the tropics. Hence, there is a serious need for robust methods for undertaking water resource assessments to underpin the sustainable management of water in tropical regions. This paper describes the largest and most comprehensive assessment of the future impacts of runoff undertaken in a tropical region using conceptual rainfall-runoff models (RRMs). Five conceptual RRMs were calibrated using data from 115 streamflow gauging stations, and model parameters were regionalized using a combination of spatial proximity and catchment similarity. Future rainfall and evapotranspiration projections (denoted here as GCMES) were transformed to catchment-scale variables by empirically scaling (ES) the historical climate series, informed by 15 global climate models (GCMs), to reflect a 1°C increase in global average surface air temperature. Using the best-performing RRM ensemble, approximately half the GCMES used resulted in a spatially averaged increase in mean annual runoff (by up to 29%%) and half resulted in a decrease (by up to 26%%). However, ~70%% of the GCMES resulted in a difference of within ±5%% of the historical rainfall (1930-2007). The range in modeled impact on runoff, as estimated by five RRMs (for individual GCMES), was compared to the range in modeled runoff using 15 GCMES (for individual RRMs). For mid- to high runoff metrics, better predictions will come from improved GCMES projections. A new finding of this study is that in the wet-dry tropics, for extremely large runoff events and low flows, improvements are needed in both GCMES and rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]

The trend analysis approach is adopted for the prediction of future climatological behavior and climate change impact on agriculture, the environment, and water resources. In this study, the innovative trend pivot analysis method (ITPAM) and trend polygon star concept method were applied for precipitation trend detection at 11 stations located in the Soan River basin (SRB), Potohar region, Pakistan. Polygon graphics of total monthly precipitation data were created and trends length and slope were calculated separately for arithmetic mean and standard deviation. As a result, the innovative methods produced useful scientific information and helped in identifying, interpreting, and calculating monthly shifts under different trend behaviors, that is, increase in some stations and decrease in others of precipitation data. This increasing and decreasing variability emerges from climate change. The risk graphs of the total monthly precipitation and monthly polygonal trends appear to show changes in the trend of meteorological data in the Potohar region of Pakistan. The monsoonal rainfall of all stations shows a complex nature of behavior, and monthly distribution is uneven. There is a decreasing trend of rainfall in high land stations of SRB with a significant change between the first dataset and the second dataset in July and August. It was examined that monsoon rainfall is increasing in lowland stations indicating a shifting pattern of monsoonal rainfall from highland to lowland areas of SRB. The increasing and decreasing trends in different periods with evidence of seasonal variations may cause irregular behavior in the water resources and agricultural sectors. Significance Statement: The monthly polygonal trends with risk graphs of total monthly precipitation data depicted a clear picture of climate change effects in the Potohar region of Pakistan. The monsoonal rainfall showed a significant decreasing trend in highland stations and an increasing trend in lowland stations, indicating a shifting pattern of monsoonal rainfall from highland to lowland areas. [ABSTRACT FROM AUTHOR]

A monthly snow accumulation and melt model was used with monthly Precipitation-elevation Regressions on Independent Slopes Model (PRISM) temperature and precipitation data to generate time series of 1 April snow water equivalent (SWE) for 1900 through 2008 in the western United States. Averaged across the western United States, SWE generally was higher than long-term (1900–2008) average conditions during the periods 1900–25, 1944–55, and 1966–82; SWE was lower than long-term average conditions during the periods 1926–43, 1957–65, and 1984–2008. During the period 1900–2008, the temporal pattern in winter precipitation exhibited wetter-than-average and drier-than-average decadal-scale periods with no long-term increasing or decreasing trend. Winter temperature generally was below average from 1900 to the mid-1950s, close to average from the mid-1950s to the mid-1980s, and above average from the mid-1980s to 2008. In general, periods of higher-than-average SWE have been associated with higher precipitation and lower temperature. Since about 1980, western U.S. winter temperatures have been consistently higher than long-term average values, and the resultant lower-than-average SWE values have been only partially offset by periods of higher-than-average precipitation. The post-1980 lower-than-average SWE conditions in the western United States are unprecedented within the context of twentieth-century climate and estimated SWE. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration's (NOAA) Big Data Partnership (BDP) was established in April 2015 through cooperative research agreements between NOAA and selected commercial and academic partners. The BDP is investigating how the value inherent in NOAA's data may be leveraged to broaden their utilization through modern cloud infrastructures and advanced "big data" techniques. NOAA's Next Generation Weather Radar (NEXRAD) data were identified as an ideal candidate for such collaborative efforts. NEXRAD Level II data are valuable yet challenging to utilize in their entirety, and recent advances in weather radar science can be applied to both the archived and real-time data streams. NOAA's National Centers for Environmental Information (NCEI) transferred the complete NEXRAD Level II historical archive, originating in 1991, through North Carolina State University's Cooperative Institute for Climate and Satellites (CICS-NC) to interested BDP collaborators. Amazon Web Services (AWS) has received and made freely available the complete archived Level II data through its AWS platform. AWS then partnered with Unidata/University Corporation for Atmospheric Research (UCAR) to establish a real-time NEXRAD feed, thereby providing on-demand dissemination of both archived and current data seamlessly through the same access mechanism by October 2015. To organize, verify, and utilize the NEXRAD data on its platform, AWS further partnered with the Climate Corporation. This collective effort among federal government, private industry, and academia has already realized a number of new and novel applications that employ NOAA's NEXRAD data, at no net cost to the U.S. taxpayer. The volume of accessed NEXRAD data, including this new AWS platform service, has increased by 130%, while the amount of data delivered by NOAA/NCEI has decreased by 50%. [ABSTRACT FROM AUTHOR]

In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA). The proposed framework produces a reconstruction of the system dynamics in a fully data-driven manner where no explicit knowledge of the dynamical model is required. Instead, a representative catalog of trajectories of the system is assumed to be available. Based on this catalog, the analog data assimilation combines the nonparametric sampling of the dynamics using analog forecasting methods with ensemble-based assimilation techniques. This study explores different analog forecasting strategies and derives both ensemble Kalman and particle filtering versions of the proposed analog data assimilation approach. Numerical experiments are examined for two chaotic dynamical systems: the Lorenz-63 and Lorenz-96 systems. The performance of the analog data assimilation is discussed with respect to classical model-driven assimilation. A Matlab toolbox and Python library of the AnDA are provided to help further research building upon the present findings. [ABSTRACT FROM AUTHOR]

Management of water resources may benefit from seasonal precipitation forecasts, but for obtaining high enough resolution, dynamical downscaling is necessary. This study investigated the downscaling capability of the Weather Research and Forecasting (WRF) Model ARW, version 3.5, on seasonal precipitation forecasts for the Hanjiang basin in China during 2001-09, which was the water source of the middle route of the South-to-North Water Diversion Project (SNWDP). The WRF Model is forced by the National Centers for Environmental Prediction Operational Climate Forecast System, version 2 (CFSv2), and it performs at a high horizontal resolution of 10 km with four selected convection schemes. The National Oceanic and Atmospheric Administration's Climate Prediction Center global daily precipitation data were employed to evaluate the WRF Model on multiple scales. On average, when large biases were removed, the WRF Model slightly outperformed the CFSv2 in all seasons, especially summer. In particular, the Kain-Fritsch convective scheme performed best in summer, whereas little difference was found in winter. The WRF Model showed similar results in monthly precipitation, but no time-dependent characteristics were observed for all months. The spatial anomaly correlation coefficient showed greater uncertainty than the bias and the temporal correlation coefficient. In addition, the performance of the WRF Model showed considerable regional variations. The upper basin always showed better agreement with observations than did the middle and lower parts of the basin. A comparison of the forecast and observed daily precipitation revealed that the WRF Model can provide more accurate extreme precipitation information than the CFSv2. [ABSTRACT FROM AUTHOR]

An object-based verification technique that keys off the radar-retrieved vertically integrated liquid (VIL) is used to evaluate how well the High-Resolution Rapid Refresh (HRRR) predicted mesoscale convective systems (MCSs) in 2012 and 2013. It is found that the modeled radar VIL values are roughly 50% lower than observed. This mean bias is accounted for by reducing the radar VIL threshold used to identify MCSs in the HRRR. This allows for a more fair evaluation of the model's skill at predicting MCSs. Using an optimized VIL threshold for each summer, it is found that the HRRR reproduces the first (i.e., counts) and second moments (i.e., size distribution) of the observed MCS size distribution averaged over the eastern United States, as well as their aspect ratio, orientation, and diurnal variations. Despite threshold optimization, the HRRR tended to predict too many (few) MCSs at lead times less (greater) than 4 h because of lead time-dependent biases in the modeled radar VIL. The HRRR predicted too many MCSs over the Great Plains and too few MCSs over the southeastern United States during the day. These biases are related to the model's tendency to initiate too many MCSs over the Great Plains and too few MCSs over the southeastern United States. Additional low biases found over the Mississippi River valley region at night revealed a tendency for the HRRR to dissipate MCSs too quickly. The skill of the HRRR at predicting specific MCS events increased between 2012 and 2013, coinciding with changes in both the model physics and in the methods used to assimilate the three-dimensional radar reflectivity. [ABSTRACT FROM AUTHOR]

On 31 May 2013, a supercell produced a tornado rated as 3 on the enhanced Fujita scale (EF3) near El Reno, Oklahoma, which was sampled by the S-band phased-array radar (PAR) at the National Weather Radar Testbed in Norman, Oklahoma. Collaboration with the forecaster who issued tornado warnings for the El Reno supercell during real-time operations focused the analysis on critical radar signatures frequently assessed during warning operations. The wealth of real-world experience provided by the forecaster, along with the quantitative analysis, highlighted differences between rapid-scan PAR data and the Weather Surveillance Radar-1988 Doppler located near Oklahoma City, Oklahoma (KTLX), within the context of forecast challenges faced on 31 May 2013. The comparison revealed that the 70-s PAR data proved most advantageous to the forecaster's situational awareness in instances of rapid storm organization, sudden mesocyclone intensification, and abrupt, short-term changes in tornado motion. Situations where PAR data were most advantageous in the depiction of storm-scale processes included 1) rapid variations in mesocyclone intensity and associated changes in inflow magnitude; 2) imminent radar-indicated development of the short-lived (EF0) Calumet, Oklahoma, and long-lived (EF3) El Reno tornadoes; and 3) precise location and motion of the tornado circulation. As a result, it is surmised that rapid-scan volumetric radar data in cases like this would augment a forecaster's ability to observe rapidly evolving storm features and deliver timely, life-saving information to the general public. [ABSTRACT FROM AUTHOR]

Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice-ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981-2010: National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP-U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice-ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation ( R = 0.82). The trend in the total ice volume in September is greatest with MERRA (−4.1 × 103 km3 decade−1) and least with CFSR (−2.7 × 103 km3 decade−1). [ABSTRACT FROM AUTHOR]

The atmospheric moisture budget and surface interactions for the southern Great Plains are evaluated for contrasting May-June periods (1998, 2002, 2006, and 2007) as background for the Cloud and Land Surface Interaction Campaign (CLASIC) of (wet) 7-30 June 2007. Budget components [flux divergence (MFD), storage change (dPW), and inflow (IF/ A)] are estimated from North American Regional Reanalysis data. Precipitation ( P) is calculated from NCEP daily gridded data, evapotranspiration ( E) is obtained as moisture budget equation residual, and the recycling ratio ( PE/ P) is estimated using a new equation. Regional averages are presented for months and five daily P categories. Monthly budget results show that E and E − P are strongly positively related to P; E − P generally is positive and balanced by positive MFD that results from its horizontal velocity divergence component (HD, positive) exceeding its horizontal advection component (HA, negative). An exception is 2007 (CLASIC), when E − P and MFD are negative and supported primarily by negative HA. These overall monthly results characterize low P days (≤0.6 mm), including for nonanomalous 2007, but weaken as daily P approaches 4 mm. In contrast, for 4 < P ≤ 8 mm day−1 E − P and MFD are moderately negative and balanced largely by negative HD except in 2007 (negative HA). This overall pattern was accentuated (including for nonanomalous 2007) when daily P > 8 mm. Daily P E/ P ratios are small and of limited range, with P category averages 0.15-0.19. Ratios for 2007 are above average only for daily P ≤ 4 mm. CLASIC wetness principally resulted from distinctive MFD characteristics. Solar radiation, soil moisture, and crop status/yield information document surface interactions. [ABSTRACT FROM AUTHOR]

This study examines the impact of climate change on droughts in Norway. A spatially distributed (1 × 1 km2) version of the Hydrologiska Byråns Vattenbalansavdelning (HBV) precipitation-runoff model was used to provide hydrological data for the analyses. Downscaled daily temperature and precipitation derived from two atmosphere-ocean general circulation models with two future emission scenarios were applied as input to the HBV model. The differences in hydroclimatological drought characteristics in the summer season between the periods 1961-90 and 2071-2100 were studied. The threshold level method was adopted to select drought events for both present and future climates. Changes in both the duration and spatial extent of precipitation, soil moisture, runoff, and groundwater droughts were identified. Despite small changes in future meteorological drought characteristics, substantial increases in hydrological drought duration and drought affected areas are expected, especially in the southern and northernmost parts of the country. Reduced summer precipitation is a major factor that affects changes in drought characteristics in the south while temperature increases play a more dominant role for the rest of the country. [ABSTRACT FROM AUTHOR]

Water vapor directly affects the dynamics of atmospheric eddy circulations through the release of latent heat. But it is difficult to include latent heat release in dynamical theories because of the associated nonlinearity (precipitation generally occurs where there is upward motion). A new effective static stability is derived that fundamentally captures the effect of latent heat release on moist eddy circulations. It differs from the usual dry static stability by an additive term that depends on temperature and a parameter measuring the up--down asymmetry of vertical velocity statistics. Latent heat release reduces the effective static stability experienced by eddies but cannot reduce it to zero so long as there are nonprecipitating regions of the eddies. Evaluation based on reanalysis data indicates that the effective static stability in the lower troposphere ranges from ∼∼80%% of the dry static stability at high latitudes to ∼∼25%% in the tropics. The effective static stability provides a solution to the longstanding problem of how to adapt dry dynamical theories to the moist circulations in the atmosphere. Its utility for climate change problems is illustrated based on simulations with an idealized general circulation model. It is shown to help account for changes in the thermal stratification of the extratropical troposphere, the extent of the Hadley cells, the intensity of extratropical transient eddies, and the extratropical eddy length. [ABSTRACT FROM AUTHOR]

This work presents a description of the 1979–2002 tropical Atlantic (TA) SST variability modes coupled to the anomalous West African (WA) rainfall during the monsoon season. The time-evolving SST patterns, with an impact on WA rainfall variability, are analyzed using a new methodology based on maximum covariance analysis. The enhanced Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) dataset, which includes measures over the ocean, gives a complete picture of the interannual WA rainfall patterns for the Sahel dry period. The leading TA SST pattern, related to the Atlantic El Niño, is coupled to anomalous precipitation over the coast of the Gulf of Guinea, which corresponds to the second WA rainfall principal component. The thermodynamics and dynamics involved in the generation, development, and damping of this mode are studied and compared with previous works. The SST mode starts at the Angola/Benguela region and is caused by alongshore wind anomalies. It then propagates westward via Rossby waves and damps because of latent heat flux anomalies and Kelvin wave eastward propagation from an off-equatorial forcing. The second SST mode includes the Mediterranean and the Atlantic Ocean, showing how the Mediterranean SST anomalies are those that are directly associated with the Sahelian rainfall. The global signature of the TA SST patterns is analyzed, adding new insights about the Pacific–Atlantic link in relation to WA rainfall during this period. Also, this global picture suggests that the Mediterranean SST anomalies are a fingerprint of large-scale forcing. This work updates the results given by other authors, whose studies are based on different datasets dating back to the 1950s, including both the wet and the dry Sahel periods. [ABSTRACT FROM AUTHOR]

The sampling error variances of the 5° × 5° Global Historical Climatological Network (GHCN) monthly surface air temperature data are estimated from January 1851 to December 2001. For each GHCN grid box and for each month in the above time interval, an error variance is computed. The authors’ error estimation is determined by two parameters: the spatial variance and a correlation factor determined by using a regression. The error estimation procedures have the following steps. First, for a given month for each grid box with at least four station anomalies, the spatial variance of the grid box’s temperature anomaly, &sigmacirc;2s, is calculated by using a 5-yr moving time window (MTW). Second, for each grid box with at least four stations, a regression is applied to find a correlation factor, αs, in the same 5-yr MTW. Third, spatial interpolation is used to fill the spatial variance and the correlation factor in grid boxes with less than four stations. Fourth, the sampling error variance is calculated by using the formula E2 = αs&sigmacirc;2s/N, where N is the total number of observations for the grid box in the given month. The two parameters of the authors’ error estimation are compared with those of the University of East Anglia’s Climatic Research Unit for the decadal data. The comparison shows a close agreement of the parameters’ values for decadal data. An advantage of this new method is the generation of monthly error estimates. The authors’ error product will be available at the U.S. National Climatic Data Center. [ABSTRACT FROM AUTHOR]

Rainfall in South America as simulated by a 24-ensemble member of the ECHAM 4.5 atmospheric general circulation model is compared and contrasted with observations (in areas in which data are available) for the period 1976–2001. Emphasis is placed on determining the onset and end of the rainy season, from which its length and rain rate are determined. It is shown that over large parts of the domain the onset and ending dates are well simulated by the model, with biases of less than 10 days. There is a tendency for model onset to occur early and ending to occur late, resulting in a simulated rainy season that is on average too long in many areas. The model wet season rain rate also tends to be larger than observed. To estimate the relative importance of errors in wet season length and rain rate in determining biases in the annual total, adjusted totals are computed by substituting both the observed climatological wet season length and rate for those of the model. Problems in the rain rate generally are more important than problems in the length. The wet season length and rain rate also contribute substantially to interannual variations in the annual total. These quantities are almost independent, and it is argued that they are each associated with different mechanisms. The observed onset dates almost always lie within the range of onset of the ensemble members, even in the areas with a large model onset bias. In some areas, though, the model does not perform well. In southern Brazil the model ensemble average onset always occurs in summer, whereas the observations show that winter is often the wettest period. Individual members, however, do occasionally show a winter rainfall peak. In southern Northeast Brazil the model has a more distinct rainy season than is observed. In the northwest Amazon the model annual cycle is shifted relative to that observed, resulting in a model bias. No interannual relationship between model and observed onset dates is expected unless onset in the model and observations has a mutual relationship with SST anomalies. In part of the near-equatorial Amazon, there does exist an interannual relationship between onset dates. Previous studies have shown that in this area there is a relationship between SST anomalies and variations in seasonal total rainfall. [ABSTRACT FROM AUTHOR]