A new evaluation of the role of urbanization to warming at various spatial scales: Evidence from the Guangdong‐Hong Kong‐Macao Region, China

The urbanization impacts on Surface Air Temperature (SAT) change in the Guangdong‐Hong Kong‐Macao region (GHMR) from 1979 to 2018 are examined using homogeneous surface observations, reanalysis, and remote sensing. Results show that the warming due to urbanization tends to be smaller or insignificant as the spatial scale increases. The urbanization contribution to the local warming can reach as high as 50% in the center of each metropolis, remains high (~25%) in the Greater Bay Area (GBA), and decreases to about 10% in the whole GHMR. The warming in GHMR is nearly uniform throughout the day, and therefore the observed trend of the Diurnal Temperature Range (DTR) is not statistically significant. However, the urbanization contribution exhibits distinct seasonal variations, large in summer and autumn while smaller in winter and spring

Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4): Part II. Parametric and Structural Uncertainty Estimations

Described herein is the parametric and structural uncertainty quantification for the monthly Extended Reconstructed Sea Surface Temperature (ERSST) version 4 (v4). A Monte Carlo ensemble approach was adopted to characterize parametric uncertainty, because initial experiments indicate the existence of significant nonlinear interactions. Globally, the resulting ensemble exhibits a wider uncertainty range before 1900, as well as an uncertainty maximum around World War II. Changes at smaller spatial scales in many regions, or for important features such as Niño-3.4 variability, are found to be dominated by particular parameter choices. Substantial differences in parametric uncertainty estimates are found between ERSST.v4 and the independently derived Hadley Centre SST version 3 (HadSST3) product. The largest uncertainties are over the mid and high latitudes in ERSST.v4 but in the tropics in HadSST3. Overall, in comparison with HadSST3, ERSST.v4 has larger parametric uncertainties at smaller spatial and shorter time scales and smaller parametric uncertainties at longer time scales, which likely reflects the different sources of uncertainty quantified in the respective parametric analyses. ERSST.v4 exhibits a stronger globally averaged warming trend than HadSST3 during the period of 1910–2012, but with a smaller parametric uncertainty. These global-mean trend estimates and their uncertainties marginally overlap. Several additional SST datasets are used to infer the structural uncertainty inherent in SST estimates. For the global mean, the structural uncertainty, estimated as the spread between available SST products, is more often than not larger than the parametric uncertainty in ERSST.v4. Neither parametric nor structural uncertainties call into question that on the global-mean level and centennial time scale, SSTs have warmed notably

An updated evaluation of the global mean land surface air temperature and surface temperature trends based on CLSAT and CMST

Past versions of global surface temperature (ST) datasets have been shown to have underestimated the recent warming trend over 1998–2012. This study uses a newly updated global land surface air temperature and a land and marine surface temperature dataset, referred to as China global land surface air temperature (C-LSAT) and China merged surface temperature (CMST), to estimate trends in the global mean ST (combining land surface air temperature and sea surface temperature anomalies) with the data uncertainties being taken into account. Comparing with existing datasets, the statistical significance of the global mean ST warming trend during the past century (1900–2017) remains unchanged, while the recent warming trend during the “hiatus” period (1998–012) increases obviously, which is statistically significant at 95% level when fitting uncertainty is considered as in previous studies (including IPCC AR5) and is significant at 90% level when both fitting and data uncertainties are considered. Our analysis shows that the global mean ST warming trends in this short period become closer among the newly developed global observational data (CMST), remotely sensed/Buoy network infilled datasets, and reanalysis data. Based on the new datasets, the warming trends of global mean land SAT as derived from C-LSAT 2.0 for the period of 1979–2019, 1951–2019, 1900–2019 and 1850–2019 were estimated to be 0.296, 0.219, 0.119 and 0.081 °C/decade, respectively. The warming trends of global mean ST as derived from CMST for the periods of 1998–2019, 1979–2019, 1951–2019 and 1900–2019 were estimated to be 0.195, 0.173, 0.145 and 0.091 °C/decade, respectively

Update on the Temperature Corrections of Global Air-Sea CO2 Flux Estimates

The oceans are a major carbon sink. Sea surface temperature (SST) is a crucial variable in the calculation of the air-sea carbon dioxide (CO2) flux from surface observations. Any bias in the SST or any upper ocean vertical temperature gradient (e.g., the cool skin effect) potentially generates a bias in the CO2 flux estimates. A recent study suggested a substantial increase (∼50% or ∼0.9 Pg C yr−1) in the global ocean CO2 uptake due to this temperature effect. Here, we use a gold standard buoy SST data set as the reference to assess the accuracy of insitu SST used for flux calculation. A physical model is then used to estimate the cool skin effect, which varies with latitude. The bias-corrected SST (assessed by buoy SST) coupled with the physics-based cool skin correction increases the average ocean CO2 uptake by ∼35% (0.6 Pg C yr−1) from 1982 to 2020, which is substantially smaller than the previous correction. After these temperature considerations, we estimate an average net ocean CO2 uptake of 2.2 ± 0.4 Pg C yr−1 from 1994 to 2007 based on an ensemble of surface observation-based flux estimates, in line with the independent interior ocean carbon storage estimate corrected for the river induced natural outgassing flux (2.1 ± 0.4 Pg C yr−1)

The Assessment of Global Surface Temperature Change from 1850s: The C-LSAT2.0 Ensemble and the CMST-Interim Datasets

Based on C-LSAT2.0, using high- and low-frequency components reconstruction methods, combined with observation constraint masking, a reconstructed C-LSAT2.0 with 756 ensemble members from the 1850s to 2018 has been developed. These ensemble versions have been merged with the ERSSTv5 ensemble dataset, and an upgraded version of the CMST-Interim dataset with 5° × 5° resolution has been developed. The CMST-Interim dataset has significantly improved the coverage rate of global surface temperature data. After reconstruction, the data coverage before 1950 increased from 78%–81% of the original CMST to 81%–89%. The total coverage after 1955 reached about 93%, including more than 98% in the Northern Hemisphere and 81%–89% in the Southern Hemisphere. Through the reconstruction ensemble experiments with different parameters, a good basis is provided for more systematic uncertainty assessment of C-LSAT2.0 and CMST-Interim. In comparison with the original CMST, the global mean surface temperatures are estimated to be cooler in the second half of 19th century and warmer during the 21st century, which shows that the global warming trend is further amplified. The global warming trends are updated from 0.085 ± 0.004°C (10 yr) −1 and 0.128 ± 0.006°C (10 yr) −1 to 0.089 ± 0.004°C (10 yr) −1 and 0.137 ± 0.007°C (10 yr) −1, respectively, since the start and the second half of 20th century

Vegetation greening offsets urbanization induced fast warming in Guangdong, Hong Kong, and Macao region (GHMR)

Previous studies show that the environment in the Guangdong, Hong Kong, and Macao region is under the double stress of global warming and urbanization. Here, we show that due to the increase of regional greenness, the effect of urbanization warming on surface air temperature (SAT) decreased with time and became statistically insignificant from 2004 to 2018, compared to 1979 onward; while the urbanization itself has significantly warmed land surface temperature (LST), with a warming rate of 0.14°C ± 0.04°C/10a at daytime and 0.02°C ± 0.02°C/10a at nighttime during 2004–2018, respectively. The anthropogenic heat was found to have a limited influence on SAT, but more significant and tangible effects on LST. It is essential to improve the control of additional warming effects caused by urbanization

Observing requirements for long-term climate records at the ocean surface

Observations of conditions at the ocean surface have been made for centuries, contributing to some of the longest instrumental records of climate change. Most prominent is the climate data record (CDR) of sea surface temperature (SST), which is itself essential to the majority of activities in climate science and climate service provision. A much wider range of surface marine observations is available however, providing a rich source of data on past climate. We present a general error model describing the characteristics of observations used for the construction of climate records, illustrating the importance of multi-variate records with rich metadata for reducing uncertainty in CDRs. We describe the data and metadata requirements for the construction of stable, multi-century marine CDRs for variables important for describing the changing climate: SST, mean sea level pressure, air temperature, humidity, winds, clouds, and waves. Available sources of surface marine data are reviewed in the context of the error model. We outline the need for a range of complementary observations, including very high quality observations at a limited number of locations and also observations that sample more broadly but with greater uncertainty. We describe how high-resolution modern records, particularly those of high-quality, can help to improve the quality of observations throughout the historical record. We recommend the extension of internationally-coordinated data management and curation to observation types that do not have a primary focus of the construction of climate records. Also recommended is reprocessing the existing surface marine climate archive to improve and quantify data and metadata quality and homogeneity. We also recommend the expansion of observations from research vessels and high quality moorings, routine observations from ships and from data and metadata rescue. Other priorities include: field evaluation of sensors; resources for the process of establishing user requirements and determining whether requirements are being met; and research to estimate uncertainty, quantify biases and to improve methods of construction of CDRs. The requirements developed in this paper encompass specific actions involving a variety of stakeholders, including funding agencies, scientists, data managers, observing network operators, satellite agencies, and international co-ordination bodies

The International Comprehensive Ocean-Atmosphere Data Set – meeting users needs and future priorities

The International Comprehensive Ocean-Atmosphere Data Set (ICOADS) is a collection and archive of in situ marine observations, which has been developed over several decades as an international project and recently guided by formal international partnerships and the ICOADS Steering Committee. ICOADS contains observations from many different observing systems encompassing the evolution of measurement technology since the 18th century. ICOADS provides an integrated source of observations for a range of applications including research and climate monitoring, and forms the main marine in situ surface data source, e.g., near-surface ocean observations and lower atmospheric marine-meteorological observations from buoys, ships, coastal stations, and oceanographic sensors, for oceanic and atmospheric research and reanalysis. ICOADS has developed ways to incorporate user and reanalyses feedback information associated with permanent unique identifiers and is also the main repository for data that have been rescued from ships’ logbooks and other marine data digitization activities. ICOADS has been adopted widely because it provides convenient access to a range of observation types, globally, and through the entire marine instrumental record. ICOADS has provided a secure home for such observations for decades. Because of the increased volume of observations, particularly those available in near-real-time, and an expansion of their diversity, the ICOADS processing system now requires extensive modernization. Based on user feedback, we will outline the improvements that are required, the challenges to their implementation, and the benefits of upgrading this important and diverse marine archive and distribution activity

A call for new approaches to quantifying biases in observations of sea-surface temperature

Global surface-temperature changes are a fundamental expression of climate change. Recent, much-debated, variations in the observed rate of surface-temperature change have highlighted the importance of uncertainty in adjustments applied to sea-surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface-temperature change and provide higher- quality gridded SST fields for use in many applications. Bias adjustments have been based either on physical models of the observing processes or on the assumption of an unchanging relationship between SST and a reference data set such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and timescales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of SST biases. Overcoming these will require clarification of past observational methods, improved modeling of biases associated with each observing method, and the development of statistical bias estimates that are less sensitive to the absence of metadata regarding the observing method. New approaches are required that embed bias models, specific to each type of observation, within a robust statistical framework. Mobile platforms and rapid changes in observation type require biases to be assessed for individual historic and present-day platforms (i.e., ships or buoys) or groups of platforms. Lack of observational metadata and of high-quality observations for validation and bias model development are likely to remain major challenges

Sea surface temperature intercomparison in the framework of the Copernicus Climate Change Service (C3S)

A joint effort between the Copernicus Climate Change Service (C3S) and the Group for High Resolution Sea Surface Temperature (GHRSST) has been dedicated to an intercomparison study of eight global gap-free Sea Surface Temperature (SST) products to assess their accurate representation of the SST relevant to climate analysis. In general, all SST products show consistent spatial patterns and temporal variability during the overlapping time period (2003-2018). The main differences between each product are located in western boundary current and Antarctic Circumpolar Current regions. Linear trends display consistent SST spatial patterns among all products and exhibit a strong warming trend from 2012 to 2018 with the Pacific Ocean basin as the main contributor. SST discrepancy between all SST products is very small compared to the significant warming trend. Spatial power spectral density shows that the interpolation into 1o spatial resolution has negligible impacts on our results. The global mean SST time series reveals larger differences among all SST products during the early period of the satellite era (1982-2002) when there were fewer observations, indicating that the observation frequency is the main constraint of the SST climatology. The maturity matrix scores, which present the maturity of each product in terms of documentation, storage, and dissemination but not the scientific quality, demonstrate that ESA-CCI and OSTIA SST are well documented for users' convenience. Improvements could be made for MGDSST and BoM SST. Finally, we have recommended that these SST products can be used for fundamental climate applications and climate studies (e.g. El Nino)