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The development of this special issue was supported by SCOR (Scientific Committee for Oceanic Research) via direct funding to the IIOE-2 and indirect funding through the IMBeR (Integrated Marine Biosphere Research) regional program SIBER (Sustained Indian Ocean Biogeochemistry and Ecosystem Research). This special issue was also supported indirectly by the IOC (Intergovernmental Oceanographic Commission of UNESCO), and the governments of India and Western Australia, through the IIOE-2 Joint...

Here, we investigate the relative roles of atmospheric nonlinearities and asymmetrical sea surface temperature (SST) forcing in the El Niño–Southern Oscillation (ENSO) asymmetrical rainfall response. Applying a vertically integrated water vapor budget to the ERA5 reanalysis leads to a simple analytical equation for precipitation anomalies. This formulation reveals that ENSO rainfall anomalies are dominated by the linear component of the dynamical term (i.e., the anomalous moisture convergence due to the effect of circulation anomalies on climatological humidity). Nonlinearities in this term and the linear thermodynamical term (i.e., the effect of climatological circulation on humidity anomalies) both strengthen central Pacific rainfall anomalies for both ENSO phases. In contrast, the nonlinear term associated with the effect of anomalous divergence on anomalous moisture (i.e., the mixed term) weakens La Niña dry and strengthens El Niño wet anomalies, in particular during extreme El Niño events when it contributes to about 40% of the eastern Pacific wet anomalies. Overall, atmospheric nonlinearities directly account for ∼70% of the positively skewed ENSO rainfall distribution east of the date line, and ∼50% of the negatively skewed rainfall distribution in the western Pacific. The remaining ENSO rainfall asymmetries are attributable to the asymmetrical ENSO SST pattern. This asymmetrical SST pattern also has contributions from atmospheric nonlinearities through the Bjerknes feedback loop, in addition to those from oceanic nonlinearities. Our estimates are thus likely a lower bound of the contribution of atmospheric nonlinearities to the overall ENSO rainfall asymmetry.

Rainfall projections from the Coupled Model Intercomparison Project (CMIP) models are strongly tied to projected Sea Surface Temperature (SST) spatial patterns through the “warmer-gets-wetter” mechanism. While these models consistently project an enhanced equatorial warming they however indicate much more uncertain changes in zonal SST gradients. That translates into large uncertainties on rainfall projections. Here, we force an atmospheric model with synthetic SSTs whose zonal SST gradient changes span the range of CMIP5 uncertainties in the presence and in the absence of the robust equatorially enhanced warming. Our results confirm that projected rainfall changes are dominated by the effect of circulation changes, which are tied to SST through the “warmer-gets-wetter” mechanism. We show that SPCZ rainfall changes are entirely driven by the uncertain zonal SST gradient changes. The western equatorial Pacific rainfall increase is largely controlled by the robust enhanced equatorial warming for modest zonal SST gradient changes. However, for larger values, the effect of the zonal SST gradient change on rainfall projections becomes dominant due to nonlinear interactions with the enhanced equatorial warming. Overall, our study demonstrates that uncertainties in the zonal SST gradient changes strongly contribute to uncertainties in rainfall projections over both the South Pacific Convergence Zone and western equatorial Pacific. It is thus critical to reduce these uncertainties to produce more robust precipitation estimates.

The purpose of this document (D4.1 Product Validation and Intercomparison Report, PVIR, document version v3.0) is to describe the results of the validation of the Sea Surface Salinity (SSS) products obtained during the ESA CCI+ SSS project when compared with other data sources. The PVIR is a requirement of the Statement of Work (Task 3 SoW ref. ESA-CCI-PRGM-EOPS-SW-17- 0032). The PVIR contains a list of all reference datasets used for validation of each SSS product. This report contains an assessment of both the level 4 and level 3 (ascending, descending and combined ascending plus descending) products for weekly and monthly time periods. The products are based on a temporal optimal interpolation of SSS data measured by SMOS, Aquarius-SAC and SMAP satellite missions. All products are gridded on an equal area EASE-2 grid with a grid resolution of ~25 km.

Sea Surface Salinity (SSS) is an increasingly used Essential Ocean and Climate Variable. The Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI + SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well-established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in-depth analysis of the measurements themselves, together with some limited use of independent reference data. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale. They display large year-to-year seasonal variations over the 2010–2019 decade, sometimes by more than ±0.4 over large regions. The robust standard deviation of the monthly CCI SSS minus in situ Argo salinities is 0.15 globally, while it is at least 0.20 with individual satellite SSS fields. r2 is 0.97, similar or better than with original datasets. The correlation with independent ship thermosalinographs SSS further highlights the CCI data set excellent performance, especially near land areas. During the SMOS-Aquarius period, when the representativity uncertainties are the largest, r2 is 0.84 with CCI while it is 0.48 with the Aquarius original data set. SSS CCI data are freely available and will be updated and extended as more satellite data become available.

Climate variability in the tropical Pacific affects global climate on a wide range of time scales. On interannual time scales, the tropical Pacific is home to the El Niño–Southern Oscillation (ENSO). Decadal variations and changes in the tropical Pacific, referred to here collectively as tropical Pacific decadal variability (TPDV), also profoundly affect the climate system. Here, we use TPDV to refer to any form of decadal climate variability or change that occurs in the atmosphere, the ocean, and over land within the tropical Pacific. “Decadal,” which we use in a broad sense to encompass multiyear through multidecadal time scales, includes variability about the mean state on decadal time scales, externally forced mean-state changes that unfold on decadal time scales, and decadal variations in the behavior of higher-frequency modes like ENSO.

Sea Surface Salinity (SSS) is an increasingly used Essential Ocean and Climate Variable. The Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI + SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well-established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in-depth analysis of the measurements themselves, together with some limited use of independent reference data. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale. They display large year-to-year seasonal variations over the 2010–2019 decade, sometimes by more than ±0.4 over large regions. The robust standard deviation of the monthly CCI SSS minus in situ Argo salinities is 0.15 globally, while it is at least 0.20 with individual satellite SSS fields. r2 is 0.97, similar or better than with original datasets. The correlation with independent ship thermosalinographs SSS further highlights the CCI data set excellent performance, especially near land areas. During the SMOS-Aquarius period, when the representativity uncertainties are the largest, r2 is 0.84 with CCI while it is 0.48 with the Aquarius original data set. SSS CCI data are freely available and will be updated and extended as more satellite data become available

The Indian Ocean Observing System (IndOOS), established in 2006, is a multi-national network of sustained oceanic measurements that underpin understanding and forecasting of weather and climate for the Indian Ocean region and beyond. Almost one-third of humanity indeed lives around the Indian Ocean, many in countries dependent on fisheries and rain-fed agriculture that are vulnerable to climate variability and extremes. The Indian Ocean alone has absorbed a quarter of the global oceanic heat uptake over the last two decades and the fate of this heat and its impact on future change is unknown. Climate models project accelerating sea level rise, more frequent extremes in monsoon rainfall, and decreasing oceanic productivity. In view of these new scientific challenges, a three-year, international review of the IndOOS by more than 60 scientific experts now highlights the need for an enhanced observing network that can better meet societal challenges, and provide more reliable forecasts. Here we present core findings from this review, including the need for 1) chemical, biological, and ecosystem measurements alongside physical parameters; 2) expansion into the western tropics to improve understanding of the monsoon circulation; 3) better resolved upper-ocean processes to improve understanding of air-sea coupling and yield better sub-seasonal to seasonal predictions; and 4) expansion into key coastal regions and the deep ocean to better constrain the basin-wide energy budget. These goals will require new agreements and partnerships with and among Indian Ocean rim countries, creating opportunities for them to enhance their monitoring and forecasting capacity as part of IndOOS-2.

This Deep-Sea Research II special issue is the third volume of scientific papers to emerge from the Second International Indian Ocean Expedition (IIOE-2, Fig. 1). This diverse body of contemporary Indian Ocean research (some motivated by IIOE-2 and others not) includes papers on physical, chemical and biological oceanography, and covers trophic levels ranging from phytoplankton to whales. With this volume the IIOE-2 takes another important step toward achieving its overarching goal, which is to “… advance our understanding of interactions between geologic, oceanic and atmospheric processes that give rise to the complex physical dynamics of the Indian Ocean region, and determine how those dynamics affect climate, extreme events, marine biogeochemical cycles, ecosystems and human populations” (Hood et al., 2015).

While tropical cyclone (TC) track forecasts have become increasingly accurate over recent decades, intensity forecasts from both numerical models and statistical schemes have been trailing behind. Most operational statistical-dynamical forecasts of TC intensity use linear regression to relate the initial TC characteristics and most relevant large-scale environmental parameters along the TC track to the TC intensification rate. Yet, many physical processes involved in TC intensification are nonlinear, hence potentially hindering the skill of those linear schemes. Here, we develop two nonlinear TC intensity hindcast schemes, for the first time globally. These schemes are based on either support vector machine (SVM) or artificial neural network (ANN) algorithms. Contrary to linear schemes, which perform slightly better when trained individually over each TC basin, nonlinear methods perform best when trained globally. Globally trained nonlinear schemes improve TC intensity hindcasts relative to regionally trained linear schemes in all TC-prone basins, especially the SVM scheme for which this improvement reaches similar to 10% globally. The SVM scheme, in particular, partially corrects the tendency of the linear scheme to underperform for moderate intensity (category 2 and less on the Saffir-Simpson scale) and decaying TCs. Although the TC intensity hindcast skill improvements described above are an upper limit of what could be achieved operationally (when using forecasted TC tracks and environmental parameters), it is comparable to that achieved by operational forecasts over the last 20 years. This improvement is sufficiently large to motivate more testing of nonlinear methods for statistical TC intensity prediction at operational centers.

This Deep-Sea Research II special issue is the second compendium of scientific papers to emerge from the Second International Indian Ocean Expedition (IIOE-2, Fig. 1). By pulling this diverse body of contemporary Indian Ocean research together (some motivated by IIOE-2 and others not) the IIOE-2 has begun the process of achieving its overarching goal, which is to “… advance our understanding of interactions between geologic, oceanic and atmospheric processes that give rise to the complex physical dynamics of the Indian Ocean region, and determine how those dynamics affect climate, extreme events, marine biogeochemical cycles, ecosystems and human populations”