Abstract

Ocean surface turbulent fluxes play an important role in the energy and water cycles of the atmosphere–ocean coupled system, and several flux products have become available in recent years. Here, turbulent fluxes from 6 widely used reanalyses, 4 satellite-derived flux products, and 2 combined product are evaluated by comparison with direct covariance latent heat (LH) and sensible heat (SH) fluxes and inertial-dissipation wind stresses measured from 12 cruises over the tropics and mid- and high latitudes. The biases range from −3.0 to 20.2 W m⁻² for LH flux, from −1.4 to 6.0 W m⁻² for SH flux, and from −7.6 to 7.9 × 10⁻³ N m⁻² for wind stress. These biases are small for moderate wind speeds but diverge for strong wind speeds (>10 m s⁻¹). The total flux biases are then further evaluated by dividing them into uncertainties due to errors in the bulk variables and the residual uncertainty. The bulk-variable-caused uncertainty dominates many products’ SH flux and wind stress biases. The biases in the bulk variables that contribute to this uncertainty can be quite high depending on the cruise and the variable. On the basis of a ranking of each product’s flux, it is found that the Modern-Era Retrospective Analysis for Research and Applications (MERRA) is among the “best performing” for all three fluxes. Also, the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and the National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) reanalysis are among the best performing for two of the three fluxes. Of the satellite-derived products, version 2b of the Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF2b) is among the best performing for two of the three fluxes. Also among the best performing for only one of the fluxes are the 40-yr ERA (ERA-40) and the combined product objectively analyzed air–sea fluxes (OAFlux). Direction for the future development of ocean surface flux datasets is also suggested.

Abstract

Reanalysis products produced at the various centers around the globe are utilized for many different scientific endeavors, including forcing land surface models and creating surface flux estimates. Here, flux tower observations of temperature, wind speed, precipitation, downward shortwave radiation, net surface radiation, and latent and sensible heat fluxes are used to evaluate the performance of various reanalysis products [NCEP–NCAR reanalysis and Climate Forecast System Reanalysis (CFSR) from NCEP; 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) from ECMWF; and Modern-Era Retrospective Analysis for Research and Applications (MERRA) and Global Land Data Assimilation System (GLDAS) from the Goddard Space Flight Center (GSFC)]. To combine the biases and standard deviation of errors from the separate stations, a ranking system is utilized. It is found that ERA-Interim has the lowest overall bias in 6-hourly air temperature, followed closely by MERRA and GLDAS. The variability in 6-hourly air temperature is again most accurate in ERA-Interim. ERA-40 is found to have the lowest overall bias in latent heat flux, followed closely by CFSR, while ERA-40 also has the lowest 6-hourly sensible heat bias. MERRA has the second lowest and is close to ERA-40. The variability in 6-hourly precipitation is best captured by GLDAS and ERA-Interim, and ERA-40 has the lowest precipitation bias. It is also found that at monthly time scales, the bias term in the reanalysis products are the dominant cause of the mean square errors, while at 6-hourly and daily time scales the dominant contributor to the mean square errors is the correlation term. Also, it is found that the hourly CFSR data have discontinuities present due to the assimilation cycle, while the hourly MERRA data do not contain these jumps.

Abstract

Skillful subseasonal prediction of extreme heat and precipitation greatly benefits multiple sectors, including water management, public health, and agriculture, in mitigating the impact of extreme events. A statistical model is developed to predict the weekly frequency of extreme warm days and 14-day standardized precipitation index (SPI) during boreal summer in the United States. We use a leading principal component of U.S. soil moisture and an index based on the North Pacific sea surface temperature (SST) as predictors. The model outperforms the NCEP Climate Forecast System, version 2 (CFSv2), at weeks 3–4 in the eastern United States. It is found that the North Pacific SST anomalies persist for several weeks and are associated with a persistent wave train pattern, which leads to increased occurrences of blocking and extreme temperature over the eastern United States. Extreme dry soil moisture conditions persist into week 4 and are associated with an increase in sensible heat flux and a decrease in latent heat flux, which may help to maintain the overlying anticyclone. The clear-sky conditions associated with blocking anticyclones further decrease soil moisture and increase the frequency of extreme warm days. This skillful statistical model has the potential to aid in irrigation scheduling, crop planning, and reservoir operation and to provide mitigation of impacts from extreme heat events.

Abstract

This study investigates the subseasonal variability of anticyclonic Rossby wave breaking (AWB) and its impacts on atmospheric circulations and tropical cyclones (TCs) over the North Atlantic in the warm season from 1985 to 2013. Significant anomalies in sea level pressure, tropospheric wind, and humidity fields are found over the tropical–subtropical Atlantic within 8 days of an AWB activity peak. Such anomalies may lead to suppressed TC activity on the subseasonal time scale, but a significant negative correlation between the subseasonal variability of AWB and Atlantic basinwide TC activity does not exist every year, likely due to the modulation of TCs by other factors. It is also found that AWB occurrence may be modulated by the Madden–Julian oscillation (MJO). In particular, AWB occurrence over the tropical–subtropical west Atlantic is reduced in phases 2 and 3 and enhanced in phases 6 and 7 based on the Real-Time Multivariate MJO (RMM) index. The impacts of AWB on the predictive skill of Atlantic TCs are examined using the Global Ensemble Forecasting System (GEFS) reforecasts with a forecast lead time up to 2 weeks. The hit rate of tropical cyclogenesis during active AWB episodes is lower than the long-term-mean hit rate, and the GEFS is less skillful in capturing the variations of weekly TC activity during the years of enhanced AWB activity. The lower predictability of TCs is consistent with the lower predictability of environmental variables (such as vertical wind shear, moisture, and low-level vorticity) under the extratropical influence.

Abstract

Atmospheric rivers (ARs) have significant hydrometeorological impacts on the U.S. West Coast. This study presents the connection between the characteristics of large-scale Rossby wave breaking (RWB) over the eastern North Pacific and the regional-scale hydrological impacts associated with landfalling ARs on the U.S. West Coast (36°–49°N). ARs associated with RWB account for two-thirds of the landfalling AR events and >70% of total AR-precipitation in the winter season. The two regimes of RWB—anticyclonic wave breaking (AWB) and cyclonic wave breaking (CWB)—are associated with different directions of the vertically integrated water vapor transport (IVT). AWB-ARs impinge in a more westerly direction on the coast whereas CWB-ARs impinge in a more southwesterly direction.

Most of the landfalling ARs along the northwestern coast of the United States (states of Washington and Oregon) are AWB-ARs. Because of their westerly impinging angles when compared to CWB-ARs, AWB-ARs arrive more orthogonally to the western Cascades and more efficiently transform water vapor into precipitation through orographic lift than CWB-ARs. Consequently, AWB-ARs are associated with the most extreme streamflows in the region.

Along the southwest coast of the United States (California), the southwesterly impinging angles of CWB-ARs are more orthogonal to the local topography. Furthermore, the southwest coast CWB-ARs have more intense IVT. Consequently, CWB-ARs are associated with the most intense precipitation. As a result, most of the extreme streamflows in southwest coastal basins are associated with CWB-ARs. In summary, depending on the associated RWB type, ARs impinge on the local topography at a different angle and have a different spatial signature of precipitation and streamflow.

Abstract

Motivated by increasing demand in the community for intraseasonal predictions of weather extremes, predictive skill of tropical cyclogenesis is investigated in this study based on a global coupled model system. Limited intraseasonal cyclogenesis prediction skill with a high false alarm rate is found when averaged over about 600 tropical cyclones (TCs) over global oceans from 2003 to 2013, particularly over the North Atlantic (NA). Relatively skillful genesis predictions with more than 1-week lead time are only evident for about 10% of the total TCs. Further analyses suggest that TCs with relatively higher genesis skill are closely associated with the Madden–Julian oscillation (MJO) and tropical synoptic waves, with their geneses strongly phase-locked to the convectively active region of the MJO and low-level cyclonic vorticity associated with synoptic-scale waves. Moreover, higher cyclogenesis prediction skill is found for TCs that formed during the enhanced periods of strong MJO episodes than those during weak or suppressed MJO periods. All these results confirm the critical role of the MJO and tropical synoptic waves for intraseasonal prediction of TC activity. Tropical cyclogenesis prediction skill in this coupled model is found to be closely associated with model predictability of several large-scale dynamical and thermodynamical fields. Particularly over the NA, higher predictability of low-level relative vorticity, midlevel humidity, and vertical zonal wind shear is evident along a tropical belt from the West Africa coast to the Caribbean Sea, in accord with more predictable cyclogenesis over this region. Over the extratropical NA, large-scale variables exhibit less predictability due to influences of extratropical systems, leading to poor cyclogenesis predictive skill.

Abstract

This study examines the dependence of surface albedo on solar zenith angle (SZA) over snow-free land surfaces using the intensive observations of surface shortwave fluxes made by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program and the National Oceanic and Atmospheric Administration Surface Radiation Budget Network (SURFRAD) in 1997–2005. Results are used to evaluate the National Centers for Environmental Prediction (NCEP) Global Forecast Systems (GFS) parameterization and several new parameterizations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) products. The influence of clouds on surface albedo and the albedo difference between morning and afternoon observations are also investigated. A new approach is taken to partition the observed upward flux so that the direct-beam and diffuse albedos can be separately computed. The study focused first on the ARM Southern Great Plains Central Facility site. It is found that the diffuse albedo prescribed in the NCEP GFS matched closely with the observations. The direct-beam albedo parameterized in the GFS is largely underestimated at all SZAs. The parameterizations derived from the MODIS product underestimated the direct-beam albedo at large SZAs and slightly overestimated it at small SZAs. Similar results are obtained from the analyses of observations at other stations. It is also found that the morning and afternoon dependencies of direct-beam albedo on SZA differ among the stations. Attempts are made to improve numerical model algorithms that parameterize the direct-beam albedo as a product of the direct-beam albedo at SZA = 60° (or the diffuse albedo), which varies with surface type or geographical location and/or season, and a function that depends only on SZA. A method is presented for computing the direct-beam albedos over these snow-free land points without referring to a particular land-cover classification scheme, which often differs from model to model.

Abstract

Three levels of process-oriented model diagnostics are applied to evaluate the Global Ensemble Forecast System version 12 (GEFSv12) reforecasts. The level-1 diagnostics are focused on model systematic errors, which reveals that precipitation onset over tropical oceans occurs too early in terms of column water vapor accumulation. Since precipitation acts to deplete water vapor, this results in prevailing negative biases of precipitable water in the tropics. It is also associated with overtransport of moisture into the mid- and upper troposphere, leading to a dry bias in the lower troposphere and a wet bias in the mid–upper troposphere. The level-2 diagnostics evaluate some major predictability sources on the extended-range time scale: the Madden–Julian oscillation (MJO) and North American weather regimes. It is found that the GEFSv12 can skillfully forecast the MJO up to 16 days ahead in terms of the Real-time Multivariate MJO indices (bivariate correlation ≥ 0.6) and can reasonably represent the MJO propagation across the Maritime Continent. The weakened and less coherent MJO signals with increasing forecast lead times may be attributed to humidity biases over the Indo-Pacific warm pool region. It is also found that the weather regimes can be skillfully predicted up to 12 days ahead with persistence comparable to the observation. In the level-3 diagnostics, we examined some high-impact weather systems. The GEFSv12 shows reduced mean biases in tropical cyclone genesis distribution and improved performance in capturing tropical cyclone interannual variability, and midlatitude blocking climatology in the GEFSv12 also shows a better agreement with the observations than in the GEFSv10.

Abstract

The World Climate Research Programme (WCRP) envisions a world “that uses sound, relevant, and timely climate science to ensure a more resilient present and sustainable future for humankind.” This bold vision requires the climate science community to provide actionable scientific information that meets the evolving needs of societies all over the world. To realize its vision, WCRP has created five Lighthouse Activities to generate international commitment and support to tackle some of the most pressing challenges in climate science today. The overarching goal of the Lighthouse Activity on Explaining and Predicting Earth System Change is to develop an integrated capability to understand, attribute, and predict annual to decadal changes in the Earth system, including capabilities for early warning of potential high impact changes and events. This article provides an overview of both the scientific challenges that must be addressed, and the research and other activities required to achieve this goal. The work is organized in three thematic areas: (i) monitoring and modeling Earth system change; (ii) integrated attribution, prediction, and projection; and (iii) assessment of current and future hazards. Also discussed are the benefits that the new capability will deliver. These include improved capabilities for early warning of impactful changes in the Earth system, more reliable assessments of meteorological hazard risks, and quantitative attribution statements to support the Global Annual to Decadal Climate Update and State of the Climate reports issued by the World Meteorological Organization.