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We describe an undocumented change in how calm periods in near-surface wind speed (and direction) observations have been encoded in a subset of global datasets of sub-daily data after 2013. This has resulted in the under-estimation of the number of calm periods for meteorological stations across much of Asia and Europe. Hence average wind speeds after 2013 have been over-estimated, affecting the assessment of changes in global stilling and reversal phenomena after this date. By addressing this encoding change we show that globally, since 2010, wind speeds have recovered by around 30% less than previously thought., ZZ was supported by the National Natural Science Foundation of China (grants no. 42071022). CAM was supported by the Spanish Ministry of Science and Innovation (RTI2018-095749-A-I00), the Valencian Regional Government (AICO/2021/023), the Leonardo grant 2021 from the BBVA Foundation, and the Interdisciplinary Thematic Platform PTI-CLIMA. CS was supported by Swedish Formas(2019-00509 and 2017-01408) and VR (2021-02163 and 2019-03954).

The objective of this study was to evaluate the performance of two commercial‐grade electromagnetic sensors in measuring soil water content for the U.S. Climate Reference Network (USCRN). One sensor was a 50‐MHz coaxial impedance dielectric sensor (model HydraProbe, Stevens Water Monitoring Systems). The second sensor was a 1‐GHz time domain reflectometry (TDR) sensor (model TDR‐315L, Acclima). There was no substantial difference between both probes in daily mean volumetric soil water content and soil temperature measurements at a 0.1‐m depth in a customized uniform loamy soil testbed in Oak Ridge, TN, from 2016 to 2018, and at select USCRN stations with a variety of soils. However, the TDR‐315L provided more representative soil water content measurements for stations with high‐clay‐content soils than the HydraProbe. These results confirm the necessity of using site‐specific soil properties for soil sensors like the HydraProbe for estimating soil water content in different soil environments. They also suggest that TDR sensors may provide an opportunity to improve USCRN soil moisture measurements in higher‐clay‐content soils.

Reanalysis products from MERRA, NCEP2, NCEP1, and ECMWF were used to force an established ocean biogeochemical model to estimate air–sea carbon fluxes (FCO2) and partial pressure of carbon dioxide (pCO2) in the global oceans. Global air–sea carbon fluxes and pCO2 were relatively insensitive to the choice of forcing reanalysis. All global FCO2 estimates from the model forced by the four different reanalyses were within 20% of in situ estimates (MERRA and NCEP1 were within 7%), and all models exhibited statistically significant positive correlations with in situ estimates across the 12 major oceanographic basins. Global pCO2 estimates were within 1% of in situ estimates with ECMWF being the outlier at 0.6%. Basin correlations were similar to FCO2. There were, however, substantial departures among basin estimates from the different reanalysis forcings. The high latitudes and tropics had the largest ranges in estimated fluxes among the reanalyses. Regional pCO2 differences among the reanalysis forcings were muted relative to the FCO2 results. No individual reanalysis was uniformly better or worse in the major oceanographic basins. The results provide information on the characterization of uncertainty in ocean carbon models due to choice of reanalysis forcing.

A new empirical approach is developed for ocean color remote sensing. Called the Empirical Satellite Radiance-In situ Data (ESRID) algorithm, the approach uses relationships between satellite water-leaving radiances and in situ data after full processing, i.e., at Level-3, to improve estimates of surface variables while relaxing requirements on post-launch radiometric re-calibration. The approach is evaluated using SeaWiFS chlorophyll, which is the longest time series of the most widely used ocean color geophysical product. The results suggest that ESRID 1) drastically reduces the bias of ocean chlorophyll, most impressively in coastal regions, 2) modestly improves the uncertainty, and 3) reduces the sensitivity of global annual median chlorophyll to changes in radiometric re-calibration. Simulated calibration errors of 1% or less produce small changes in global median chlorophyll (less than 2.7%). In contrast, the standard NASA algorithm set is highly sensitive to radiometric calibration: similar 1% calibration errors produce changes in global median chlorophyll up to nearly 25%. We show that 0.1% radiometric calibration error (about 1% in water-leaving radiance) is needed to prevent radiometric calibration errors from changing global annual median chlorophyll more than the maximum interannual variability observed in the SeaWiFS 9-year record (+/- 3%), using the standard method. This is much more stringent than the goal for SeaWiFS of 5% uncertainty for water leaving radiance. The results suggest ocean color programs might consider less emphasis of expensive efforts to improve post-launch radiometric re-calibration in favor of increased efforts to characterize in situ observations of ocean surface geophysical products. Although the results here are focused on chlorophyll, in principle the approach described by ESRID can be applied to any surface variable potentially observable by visible remote sensing.

Ocean phytoplankton, detrital material, and water absorb and scatter light spectrally. The Ocean- Atmosphere Spectral Irradiance Model (OASIM) is intended to provide surface irradiance over the oceans with sufficient spectral resolution to support ocean ecology, biogeochemistry, and heat exchange investigations, and of sufficient duration to support inter-annual and decadal investigations. OASIM total surface irradiance (integrated 200 nm to 4 microns) was compared to in situ data and three publicly available global data products at monthly 1-degree resolution. OASIM spectrally-integrated surface irradiance had root mean square (RMS) difference= 20.1 W/sq m (about 11%), bias=1.6 W/sq m (about 0.8%), regression slope= 1.01 and correlation coefficient= 0.89, when compared to 2322 in situ observations. OASIM had the lowest bias of any of the global data products evaluated (ISCCP-FD, NCEP, and ISLSCP 11), and the best slope (nearest to unity). It had the second best RMS, and the third best correlation coefficient. OASIM total surface irradiance compared well with ISCCP-FD (RMS= 20.7 W/sq m; bias=-11.4 W/sq m, r=0.98) and ISLSCP II (RMS =25.2 W/sq m; bias= -13.8 W/sq m; r=0.97), but less well with NCEP (RMS =43.0 W/sq m ;bias=-22.6 W/sq m; x=0.91). Comparisons of OASIM photosynthetically available radiation (PAR) with PAR derived from SeaWiFS showed low bias (-1.8 mol photons /sq m/d, or about 5%), RMS (4.25 mol photons /sq m/d ' or about 12%), near unity slope (1.03) and high correlation coefficient (0.97). Coupled with previous estimates of clear sky spectral irradiance in OASIM (6.6% RMS at 1 nm resolution), these results suggest that OASIM provides reasonable estimates of surface broadband and spectral irradiance in the oceans, and can support studies on ocean ecosystems, carbon cycling, and heat exchange.

Although modem ocean color sensors, such as MODIS and SeaWiFS are often considered global missions, in reality it takes many days, even months, to sample the ocean surface enough to provide complete global coverage. The irregular temporal sampling of ocean color sensors can produce biases in monthly and annual mean chlorophyll estimates. We quantified the biases due to sampling using data assimilation to create a "truth field", which we then sub-sampled using the observational patterns of MODIS and SeaWiFS. Monthly and annual mean chlorophyll estimates from these sub-sampled, incomplete daily fields were constructed and compared to monthly and annual means from the complete daily fields of the assimilation model, at a spatial resolution of 1.25deg longitude by 0.67deg latitude. The results showed that global annual mean biases were positive, reaching nearly 8% (MODIS) and >5% (SeaWiFS). For perspective the maximum interannual variability in the SeaWiFS chlorophyll record was about 3%. Annual mean sampling biases were low (

Coccolithophores are important ecological and geochemical components of the global oceans. A global three-dimensional model was used to simulate their distributions in a multi-phytoplankton context. The realism of the simulation was supported by comparisons of model surface nutrients and total chlorophyll with in situ and satellite observations. Nitrate, silica, and dissolved iron surface distributions were positively correlated with in situ data across major oceanographic basins. Global annual departures were +18.9% for nitrate (model high), +5.4% for silica, and +45.0% for iron. Total surface chlorophyll was also positively correlated with satellite and in situ data sets across major basins. Global annual departures were −8.0% with Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (model low), +1.1% with Aqua, and −17.1% with in situ data. Global annual primary production estimates were within 1% and 9% of estimates derived from SeaWiFS and Aqua, respectively, using a common primary production algorithm. Coccolithophore annual mean relative abundances were 2.6% lower than observations, but were positively correlated across basins. Two of the other three phytoplankton groups, diatoms and cyanobacteria, were also positively correlated with observations. Distributions of coccolithophores were dependent upon interactions and competition with the other phytoplankton groups. In this model, coccolithophores had a competitive advantage over diatoms and chlorophytes by virtue of a greater ability to utilize nutrients and light at low values. However, their higher sinking rates placed them at a disadvantage when nutrients and light were plentiful. In very low nutrient conditions, such as the mid-ocean gyres, coccolithophores were unable to compete with the efficient nutrient utilization capability and low sinking rate of cyanobacteria. Comparisons of simulated coccolithophore distributions with satellite-derived estimates of calcite concentration and coccolithophore blooms showed some agreement, but also areas of departure. Vast blooms observed in the North Atlantic were well-represented by the model. However, model coccolithophores were nearly absent in the North Pacific, while calcite estimates suggested widespread abundance in summer. In situ observations supported the satellite calcite, suggesting a deficiency in the model. New satellite estimates of phytoplankton groups indicated good agreement of diatoms in one case, and poor agreement in general in another. Comparisons of phytoplankton group primary production with other models showed wide disparity. The divergence among models and satellite estimates is common for such an emerging field of research. The quantitative comparisons with in situ observations are encouraging, but disparities with model and satellite estimates suggested that further research is needed.

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chlorophyll data set was compared to comprehensive archives of in situ chlorophyll data from NASA and NOAA. The global comparison indicated a root mean square (RMS) log error of 31%, with a coefficient of determination ( r 2 ) of 0.76, using 4168 data points where in situ and SeaWiFS data were coincident and collocated. RMS log error for open ocean (defined as bottom depth>200 m) was 27.7% with r 2 =0.72, compared to 33% RMS log error and r 2 =0.60 on the coasts, indicating a deterioration of quality of the SeaWiFS data set in coastal regions. All of the Pacific oceanographic basins generally showed very good agreement with SeaWiFS, as did the South Atlantic basin. However, poorer agreement was found in the Mediterranean/Black Seas, Equatorial Atlantic, and the Antarctic. Optical complexity arising from riverine inputs, Saharan dust, and anomalous oceanic constituents contributed to the differences observed in the Atlantic, where an overestimation by SeaWiFS occurred. The Antarctic indicated a pronounced negative bias, indicating an underestimation, especially for chlorophyll concentrations greater than about 0.15 mg m −3 . The results provide a comprehensive global and geographic analysis of the SeaWiFS data set, which will assist data users and policy makers in assessing the uncertainty of estimates of global and regional ocean chlorophyll and primary production.

The JGOFS program and NASA ocean-color satellites have provided a wealth of data that can be used to test and validate models of ocean biogeochemistry. A coupled three-dimensional general circulation, biogeochemical, and radiative model of the global oceans was validated using these in situ data sources and satellite data sets. Biogeochemical processes in the model were determined from the influences of circulation and turbulence dynamics, irradiance availability, and the interactions among four phytoplankton functional groups (diatoms, chlorophytes, cyanobacteria, and coccolithophores) and four nutrients (nitrate, ammonium, silica, and dissolved iron). Annual mean log-transformed dissolved iron concentrations in the model were statistically positively correlated on basin scale with observations ðPo0:05Þ over the eight (out of 12) major oceanographic basins where data were available. The model tended to overestimate in situ observations, except in the Antarctic where a large underestimate occurred. Inadequate scavenging and excessive remineralization and/or regeneration were possible reasons for the overestimation. Basin scale model chlorophyll seasonal distributions were positively correlated with SeaWiFS chlorophyll in each of the 12 oceanographic basins ðPo0:05Þ: The global mean difference was 3.9% (model higher than SeaWiFS). The four phytoplankton groups were initialized as homogeneous and equal distributions throughout the model domain. After 26 years of simulation, they arrived at reasonable distributions throughout the global oceans: diatoms predominated high latitudes, coastal, and equatorial upwelling areas, cyanobacteria predominated the mid-ocean gyres, and chlorophytes and coccolithophores represented transitional assemblages. Seasonal patterns exhibited a range of relative responses: from a seasonal succession in the North Atlantic with coccolithophores replacing diatoms as the dominant group in mid-summer, to successional patterns with cyanobacteria replacing diatoms in mid-summer in the central North Pacific. Diatoms were associated with regions where nutrient availability was high. Cyanobacteria predominated in quiescent regions with low nutrients. While the overall patterns of phytoplankton functional group distributions exhibited broad qualitative agreement with in situ data, quantitative comparisons were mixed. Three of the four phytoplankton groups exhibited statistically significant correspondence across basins. Diatoms did not. Some basins exhibited excellent correspondence, while most showed moderate agreement, with two functional groups in agreement with data and the other two in disagreement.

[1] Two ocean color missions, SeaWiFS and MODIS, overlap in time and are processed with consistent methods. Global annual median chlorophyll from SeaWiFS and MODIS differ by 12.2%. These discrepancies exceed the maximum observed interannual variability globally and in every major oceanographic basin. Estimates of trends are affected as well. For 1998–2007 the SeaWiFS global trend is −2.6% (not statistically significant). Substitution of MODIS for SeaWiFS in 2003–2007 produces a −18% significant trend. A new approach that incorporates in situ data improves the consistency of the two sensor data sets. The global difference is −0.6% and the 10-year trend of SeaWiFS and MODIS agrees with standalone SeaWiFS (−3.3%, not significant). In oceanographic basins where sampling biases are small the differences are less than the maximum observed interannual variability. The approach improves the consistency of multiple successive ocean color missions and represents a step toward scientifically reliable Climate Data Records.

[1] A 6-year time series of remotely-sensed global ocean chlorophyll was evaluated using linear regression analysis to assess recent trends. Global ocean chlorophyll has increased 4.1% (P < 0.05). Most of the increase has occurred in coastal regions, defined as bottom depth < 200 m, where an increase of 10.4% was observed. The main contributors to the increase were the Patagonian Shelf, Bering Sea, and the eastern Pacific, southwest African, and Somalian coasts. Although the global open ocean exhibited no significant change, 4 of the 5 mid-ocean gyres (Atlantic and Pacific) showed declines in chlorophyll over the 6 years. In all but the North Atlantic gyre, these were associated with significant increases in sea surface temperature in at least one season. These results suggest that changes are occurring in the biology of the global oceans.

[1] Satellite-in situ blended ocean chlorophyll records indicate that global ocean annual primary production has declined more than 6% since the early 1980's. Nearly 70% of the global decadal decline occurred in the high latitudes. In the northern high latitudes, these reductions in primary production corresponded with increases in sea surface temperature and decreases in atmospheric iron deposition to the oceans. In the Antarctic, the reductions were accompanied by increased wind stress. Three of four low latitude basins exhibited decadal increases in annual primary production. These results indicate that ocean photosynthetic uptake of carbon may be changing as a result of climatic changes and suggest major implications for the global carbon cycle.

Satellite observations of global ocean chlorophyll span more than two decades. However, incompatibilities between processing algorithms prevent us from quantifying natural variability. We applied a comprehensive reanalysis to the Coastal Zone Color Scanner (CZCS) archive, called the National Oceanic and Atmospheric Administration and National Aeronautics and Space Administration (NOAA-NASA) CZCS reanalysis (NCR) effort. NCR consisted of (1) algorithm improvement (AI), where CZCS processing algorithms were improved with modernized atmospheric correction and bio-optical algorithms and (2) blending where in situ data were incorporated into the CZCS AI to minimize residual errors. Global spatial and seasonal patterns of NCR chlorophyll indicated remarkable correspondence with modern sensors, suggesting compatibility. The NCR permits quantitative analyses of interannual and interdecadal trends in global ocean chlorophyll.