Explore the vast range of titles available.

Sixteen monthly air–sea heat flux products from global ocean/coupled reanalyses are compared over 1993–2009 as part of the Ocean Reanalysis Intercomparison Project (ORA-IP). Objectives include assessing the global heat closure, the consistency of temporal variability, comparison with other flux products, and documenting errors against in situ flux measurements at a number of OceanSITES moorings. The ensemble of 16 ORA-IP flux estimates has a global positive bias over 1993–2009 of 4.2 ± 1.1 W m −2 . Residual heat gain (i.e., surface flux + assimilation increments) is reduced to a small positive imbalance (typically, +1–2 W m −2 ). This compensation between surface fluxes and assimilation increments is concentrated in the upper 100 m. Implied steady meridional heat transports also improve by including assimilation sources, except near the equator. The ensemble spread in surface heat fluxes is dominated by turbulent fluxes (>40 W m −2 over the western boundary currents). The mean seasonal cycle is highly consistent, with variability between products mostly <10 W m −2 . The interannual variability has consistent signal-to-noise ratio (~2) throughout the equatorial Pacific, reflecting ENSO variability. Comparisons at tropical buoy sites (10°S–15°N) over 2007–2009 showed too little ocean heat gain (i.e., flux into the ocean) in ORA-IP (up to 1/3 smaller than buoy measurements) primarily due to latent heat flux errors in ORA-IP. Comparisons with the Stratus buoy (20°S, 85°W) over a longer period, 2001–2009, also show the ORA-IP ensemble has 16 W m −2 smaller net heat gain, nearly all of which is due to too much latent cooling caused by differences in surface winds imposed in ORA-IP.

This paper gives an overview of 20 years of research on the energy balance closure problem. It will be shown that former assumptions that measuring errors or storage terms are the reason for the unclosed energy balance do not stand up because even turbulent fluxes derived from documented methods and calibrated sensors, net radiation, and ground heat fluxes cannot close the energy balance. Instead, exchange processes on larger scales of the heterogeneous landscape have a significant influence. By including these fluxes, the energy balance can be approximately closed. Therefore, the problem is a scale problem and has important consequences to the measurement and modeling of turbulent fluxes.

Measurements made in the Columbia River basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modeled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow, which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin-Obukhov similarity; and (ii) a modified Priestley-Taylor (P-T) algorithm with physically based ecophysiological constraints, which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various time scales based on the modeled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP2, a hybrid approach can be used for similar flux-tower sites and field campaigns.

We provide a brief invitation to the novel understanding [1, 2, 3, 4] of anyonic topological order in fractional quantum (anomalous) Hall systems, via “extraordinary” quantization of effective magnetic flux in Cohomotopy — following our presentation at ISQS29 [5].

Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles in both future climate projections and weather forecasts. Evaluation of models' ability to do this is performed in a wide range of simulation environments, often without explicit consideration of the degree of observational constraint or uncertainty and typically without quantification of benchmark performance expectations. We describe a Model Intercomparison Project (MIP) that attempts to resolve these shortcomings, comparing the surface turbulent heat flux predictions of around 20 different land models provided with in situ meteorological forcing evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance-based flux tower sites. Predictions from seven out-of-sample empirical models are used to quantify the information available to land models in their forcing data and so the potential for land model performance improvement. Sites with unusual behaviour, complicated processes, poor data quality, or uncommon flux magnitude are more difficult to predict for both mechanistic and empirical models, providing a means of fairer assessment of land model performance. When examining observational uncertainty, model performance does not appear to improve in low-turbulence periods or with energy-balance-corrected flux tower data, and indeed some results raise questions about whether the energy balance correction process itself is appropriate. In all cases the results are broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. In all but two cases, latent heat flux and net ecosystem exchange of CO2 are better predicted by land models than sensible heat flux, despite it seeming to have fewer physical controlling processes. Land models that are implemented in Earth system models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. The approach we outline enables isolation of the locations and conditions under which model developers can know that a land model can improve, allowing information pathways and discrete parameterisations in models to be identified and targeted for future model development.

Accurate observational estimation of the ocean surface heat, momentum, and freshwater fluxes is crucial for studies of the global climate system. Estimating surface flux using satellite remote sensing techniques is one possible answer to this challenge. In this paper, we introduce J-OFURO3, a third-generation data set developed by the Japanese Ocean Flux Data Sets with Use of Remote-Sensing Observations (J-OFURO) research project, which represents a significant improvement from older data sets as the result of research and development conducted from several perspectives. J-OFURO3 offers data sets for surface heat, momentum, freshwater fluxes, and related parameters over the global oceans (except regions of sea ice) from 1988 to 2013. The surface flux data, based on a 0.25° grid system, have a higher spatial resolution and are more accurate than the previous efforts. This has been achieved through the adopting of the state-of-the-art algorithms that estimate the near-surface air specific humidity and the improvement of techniques using observations from multi-satellite sensors. Comparisons with in situ observations using a systematic system developed along with the J-OFURO3 data set confirmed these improvements in accuracy, as did comparisons with other data sets. J-OFURO3 data are of good quality, facilitating a clearer understanding of more fine-scale ocean–atmosphere features (such as ocean fronts, mesoscale eddies, and geographic features) and their effects on surface fluxes. The information contained in this long-term (26 year) data set is demonstrably beneficial to understanding climate change and its relationship to oceans and the atmosphere.

Understanding surface heat flux is essential for evaluating geothermal system dynamics and resource potential, particularly in tectonically active but non‐volcanic regions. This study presents the first integrated assessment of surface heat flux in three representative geothermal areas in Taxkorgan County, western China, including Taheman (THM), Liaoyangyuan (LYY) and Dabudar (DBD). Field measurements were conducted using a combination of soil temperature gradient analysis, water vapor flux estimation, soil CO2 flux measurement and the desiccant‐based CO2:H2O ratio determination method. Sequential Gaussian simulation of the geostatistical method was applied to mapping spatial distributions of heat flux and CO2 flux. The results show that the total surface heat fluxes of THM, DBD, and LYY are 68.6, 19.2, and 57.7 W·m−2, respectively, with CO2‐derived heat flux accounting for 51.8%–54.6% of the total, highlighting the dominance of gas‐phase convective heat release. THM exhibits the highest thermal output (1.3 MW), driven by deep volatile exsolution and enhanced fault permeability, characteristic of a fracture‐controlled geothermal reservoir. In contrast, LYY shows a high thermal gradient and dominant conductive heat flux, reflecting a shallow heat source. DBD displays the lowest heat flux, possibly representing a peripheral or capped thermal zone. These findings provide critical measured data and theoretical basis for the identified hybrid heat transport mechanisms and also offer broader implications for understanding tectonically controlled geothermal systems in continental collision orogenic belts.

In this paper, an automated algorithm is developed, which is used to identify the spectral gap during the heavy haze pollution process, reconstruct acquired data, and obtain pure turbulence data. Comparisons of the reconstructed turbulent flux and eddy covariance (EC) flux show that there are overestimations regarding the exchange between the surface and the atmosphere during heavy haze pollution episodes. After reconstruction via the automated algorithm, pure turbulence data can be obtained. We introduce a definition to characterize the local intermittent strength of turbulence (LIST). The trend in the LIST during pollution episodes shows that when pollution is more intense, the LIST is smaller, and intermittency is stronger; when pollution is weaker, the LIST is larger, and intermittency is weaker. At the same time, the LIST at the city site is greater than at the suburban site, which means that intermittency over the complex city area is weaker than over the flat terrain area. Urbanization seems to reduce intermittency during heavy haze pollution episodes, which means that urbanization reduces the degree of weakening in turbulent exchange during pollution episodes. This result is confirmed by comparing the average diurnal variations in turbulent fluxes at urban and suburban sites during polluted and clean periods. The sensible heat flux, latent heat flux, momentum flux, and turbulent kinetic energy (TKE) in urban and suburban areas are all affected when pollution occurs. Material and energy exchanges between the surface and the atmosphere are inhibited. Moreover, the impact of the pollution process on suburban areas is much greater than on urban areas. The turbulent effects caused by urbanization seem to help reduce the consequences of pollution under the same weather and pollution source condition, because the turbulence intermittency is weaker, and the reduction in turbulence exchange is smaller over the urban underlying surface.

Increased heat-wave frequency across the United States has led to the need for improved predictability of heat-wave events. A detailed understanding of land–atmosphere interactions and the relationship between soil moisture and temperature extremes could provide useful information for prediction. This study identifies, for many locations, a threshold of soil moisture below which there is an increase in the sensitivity of atmospheric temperature to declining soil moisture. This shift to a hypersensitive regime causes the atmosphere to be more susceptible to atmospherically driven heat-wave conditions. The soil moisture breakpoint where the regime shift occurs is estimated using segmented regression applied to observations and reanalysis data. It is shown that as the soil gets drier, there is a concomitant change in the rate of decrease in latent heat flux and increase in sensible heat flux leading to a strong positive feedback of increased air temperature near the surface, which further dries out the soil. Central, southwestern, and southeastern parts of the United States seem to have regions of clear regime shifts, while the eastern part of the United States generally does not get dry enough to reveal significant breakpoints. Sensible heat flux is seen to be a primary driver of this increased temperature sensitivity aided by the drop in latent heat flux. An investigation of flux tower sites verifies the breakpoint–flux relationships found in reanalysis data. Accurate estimation of these breakpoints can contribute to improved heat-wave prediction.

We have estimated global air–sea CO2 fluxes (fgCO2) from the open ocean to coastal seas. Fluxes and associated uncertainty are computed from an ensemble-based reconstruction of CO2 sea surface partial pressure (pCO2) maps trained with gridded data from the Surface Ocean CO2 Atlas v2020 database. The ensemble mean (which is the best estimate provided by the approach) fits independent data well, and a broad agreement between the spatial distribution of model–data differences and the ensemble standard deviation (which is our model uncertainty estimate) is seen. Ensemble-based uncertainty estimates are denoted by ±1σ. The space–time-varying uncertainty fields identify oceanic regions where improvements in data reconstruction and extensions of the observational network are needed. Poor reconstructions of pCO2 are primarily found over the coasts and/or in regions with sparse observations, while fgCO2 estimates with the largest uncertainty are observed over the open Southern Ocean (44∘ S southward), the subpolar regions, the Indian Ocean gyre, and upwelling systems.Our estimate of the global net sink for the period 1985–2019 is 1.643±0.125 PgC yr-1 including 0.150±0.010 PgC yr-1 for the coastal net sink. Among the ocean basins, the Subtropical Pacific (18–49∘ N) and the Subpolar Atlantic (49–76∘ N) appear to be the strongest CO2 sinks for the open ocean and the coastal ocean, respectively. Based on mean flux density per unit area, the most intense CO2 drawdown is, however, observed over the Arctic (76∘ N poleward) followed by the Subpolar Atlantic and Subtropical Pacific for both open-ocean and coastal sectors. Reconstruction results also show significant changes in the global annual integral of all open- and coastal-ocean CO2 fluxes with a growth rate of +0.062±0.006 PgC yr-2 and a temporal standard deviation of 0.526±0.022 PgC yr-1 over the 35-year period. The link between the large interannual to multi-year variations of the global net sink and the El Niño–Southern Oscillation climate variability is reconfirmed.