Wenying Su

Climate assessments consolidate our understanding of possible future climate conditions as represented by climate projections, which are largely based on the output of global climate models. Over the past 30 years, the scientific insights gained from climate projections have been refined through model structural improvements, emerging constraints on climate feedbacks, and increased computational efficiency. Within the same period, the process of assessing and evaluating information from climate projections has become more defined and targeted to inform users. As the size and audience of climate assessments has expanded, the framing, relevancy, and accessibility of projections has become increasingly important. This paper reviews the use of climate projections in national climate assessments (NCA) while highlighting challenges and opportunities that have been identified over time. Reflections and lessons learned address the continuous process to understand the broadening assessment audience and evolving user needs. Insights for future NCA development include (1) identifying benchmarks and standards for evaluating downscaled datasets, (2) expanding efforts to gather research gaps and user needs to inform how climate projections are presented in the assessment (3) providing practitioner guidance on the use, interpretation, and reporting of climate projections and uncertainty to better inform decision-making.

Uncertainty in Arctic top‐of‐atmosphere (TOA) radiative flux observations stems from the low sun angles and the heterogeneous scenes. Advancing our understanding of the Arctic climate system requires improved TOA radiative fluxes. We compare Cloud and Earth's Radiant Energy System (CERES) TOA radiative fluxes with Arctic Radiation‐IceBridge Sea and Ice Experiment (ARISE) airborne measurements using two approaches: grid box averages and instantaneously matched footprints. Both approaches indicate excellent agreement in the longwave and good agreement in the shortwave (SW), within 2σ uncertainty considering all error sources (CERES and airborne radiometer calibration, inversion, and sampling). While the SW differences are within 2σ uncertainty, both approaches show a ∼−10 W m⁻² average CERES‐aircraft flux difference. Investigating the source of this negative difference, we find a substantial sensitivity of the flux differences to the sea ice concentration data set. Switching from imager‐based to passive microwave‐based sea ice data in the CERES inversion process reduces the differences in the grid box average fluxes and in the sea ice partly cloudy scene anisotropy in the matched footprints. In the long‐term, more accurate sea ice concentration data are needed to reduce CERES TOA SW flux uncertainties. Switching from imager to passive microwave sea ice data, in the short‐term, could improve CERES TOA SW fluxes in polar regions, additional testing is required. Our analysis indicates that calibration and sampling uncertainty limit the ability to place strong constraints (<±7%) on CERES TOA fluxes with aircraft measurements.

Uncertainty in Arctic top-of-atmosphere (TOA) radiative flux observations stems from the low sun angles and the heterogeneous scenes. Advancing our understanding of the Arctic climate system requires improved TOA radiative fluxes. We compare Cloud and Earth’s Radiant Energy System (CERES) TOA radiative fluxes with Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne measurements using two approaches: grid box averages and instantaneously-matched footprints. Both approaches indicate excellent agreement in the longwave and good agreement in the shortwave, within 2 uncertainty considering all error sources (CERES and airborne radiometer calibration, inversion, and sampling). While the SW differences are within 2 uncertainty, both approaches show a ~‑10 W m ^‑2 average CERES-aircraft flux difference. Investigating the source of this negative difference, we find a substantial sensitivity of the flux differences to the sea ice concentration dataset. Switching from imager-based to passive microwave-based sea ice data in the CERES inversion process reduces the differences in the grid box average fluxes and in the sea ice partly cloudy scene anisotropy in the matched footprints. In the long-term, more accurate sea ice concentration data are needed to reduce CERES TOA SW flux uncertainties. Switching from imager to passive microwave sea ice data, in the short-term, could improve CERES TOA SW fluxes in polar regions, additional testing is required. Our analysis indicates that calibration and sampling uncertainty limit the ability to place strong constraints (<±7%) on CERES TOA fluxes with aircraft measurements.

Retroreflective color plates are important carriers for transmitting retroreflective chromaticity, and they can be used to calibrate portable retroreflection colorimeters. Measurement errors in the chromaticity measurements of retroreflective color plates create uncertainty of the retroreflective chromaticity coordinates. To study the uncertainty in the measurements of retroreflective chromaticity plates, we analyzed the uncertainty sources and evaluated the uncertainty of the chromaticity coordinates of a retroreflective color plate measured using a remote spectrum measurement system. The results showed that for a confidence probability p = 95% and coverage factor k = 2, the expanded uncertainties of the chromaticity coordinates of the retroreflective color plate, U(x) and U(y), were both 0.01. The evaluation results showed that the uncertainty of the spectrometer contributed to the uncertainty the most, mainly because the large uncertainty components arose from the measurements of the retroreflective color plate and the standard white plate.

One of the most crucial tasks of measuring top-of-atmosphere (TOA) radiative flux is to understand the relationships between radiances and fluxes, particularly for the reflected shortwave (SW) fluxes. The radiance-to-flux conversion is accomplished by constructing angular distribution models (ADMs). This conversion depends on solar-viewing geometries as well as the scene types within the field of view. To date, the most comprehensive observation-based ADMs are developed using the Clouds and the Earth’s Radiant Energy System (CERES) observations. These ADMs are used to derive TOA SW fluxes from CERES and other Earth radiation budget instruments which observe the Earth mostly from side-scattering angles. The Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory observes the Earth at the Lagrange-1 point in the near-backscattering directions and offers a testbed for the CERES ADMs. As the EPIC relative azimuth angles change from 168^◦ to 178^◦, the global daytime mean SW radiances can increase by as much as 10% though no notable cloud changes are observed. The global daytime mean SW fluxes derived after considering the radiance anisotropies at relative azimuth angles of 168^◦ and 178^◦ show much smaller differences (−2 with root-mean-square errors less than 3.0 Wm⁻². Consistency between SW fluxes from EPIC and CERES inverted from very different viewing geometries indicates that the CERES ADMs accurately quantify the radiance anisotropy and can be used for flux inversion from different viewing perspectives.

Biases in aerosol optical depths (AOD) and land surface albedos in the AeroCom models are manifested in the top‐of‐atmosphere (TOA) clear‐sky reflected shortwave (SW) fluxes. Biases in the SW fluxes from AeroCom models are quantitatively related to biases in AOD and land surface albedo by using their radiative kernels. Over ocean, AOD contributes about 25% to the S–N mean SW flux bias for the multi‐model mean (MMM) result. Over land, AOD and land surface albedo contribute about 40% and 30%, respectively, to the S–N mean SW flux bias for the MMM result. Furthermore, the spatial patterns of the SW flux biases derived from the radiative kernels are very similar to those between models and CERES observation, with the correlation coefficient of 0.6 over ocean and 0.76 over land for MMM using data of 2010. Satellite data used in this evaluation are derived independently from each other, consistencies in their bias patterns when compared with model simulations suggest that these patterns are robust. This highlights the importance of evaluating related variables in a synergistic manner to provide an unambiguous assessment of the models, as results from single parameter assessments are often confounded by measurement uncertainty. Model biases in land surface albedos can and must be corrected to accurately calculate TOA flux. We also compare the AOD trend from three models with the observation‐based counterpart. These models reproduce all notable trends in AOD except the decreasing trend over eastern China and the adjacent oceanic regions due to limitations in the emission data set.

Observing the Earth radiation budget (ERB) from satellites is crucial for monitoring and understanding Earth’s climate. One of the major challenges for ERB observations, particularly for reflected shortwave radiation, is the conversion of the measured radiance to the more energetically relevant quantity of radiative flux, or irradiance. This conversion depends on the solar-viewing geometry and the scene composition associated with each instantaneous observation. We first outline the theoretical basis for algorithms to convert shortwave radiance to irradiance, most commonly known as empirical angular distribution models (ADMs). We then review the progression from early ERB satellite observations that applied relatively simple ADMs, to current ERB satellite observations that apply highly sophisticated ADMs. A notable development is the dramatic increase in the number of scene types, made possible by both the extended observational record and the enhanced scene information now available from collocated imager information. Compared with their predecessors, current shortwave ADMs result in a more consistent average albedo as a function of viewing zenith angle and lead to more accurate instantaneous and mean regional irradiance estimates. One implication of the increased complexity is that the algorithms may not be directly applicable to observations with insufficient accompanying imager information, or for existing or new satellite instruments where detailed scene information is not available. Recent advances that complement and build on the base of current approaches, including machine learning applications and semi-physical calculations, are highlighted.

Biases in aerosol optical depths (AOD) and land surface albedos in the AeroCom models are manifested in the top-of-atmosphere (TOA) clear-sky reflected shortwave (SW) fluxes. Biases in the SW fluxes from AeroCom models are quantitatively related to biases in AOD and land surface albedo by using their radiative kernels. Over ocean, AOD contributes about 25% to the 60°S-60°N mean SW flux bias for the multi-model mean (MMM) result. Over land, AOD and land surface albedo contribute about 40% and 30%, respectively, to the 60°S-60°N mean SW flux bias for the MMM result. Furthermore, the spatial patterns of the SW flux biases derived from the radiative kernels are very similar to those between models and CERES observation, with the correlation coefficient of 0.6 over ocean and 0.76 over land for MMM using data of 2010. Satellite data used in this evaluation are derived independently from each other, consistencies in their bias patterns when compared with model simulations suggest that these patterns are robust. This highlights the importance of evaluating related variables in a synergistic manner to provide an unambiguous assessment of the models, as results from single parameter assessments are often confounded by measurement uncertainty. We also compare the AOD trend from three models with the observation-based counterpart. These models reproduce all notable trends in AOD (i.e. decreasing trend over eastern United States and increasing trend over India) except the decreasing trend over eastern China and the adjacent oceanic regions due to limitations in the emission dataset.

A new method for determining clear‐sky shortwave aerosol direct radiative effects (ADRE) from the Clouds and the Earth's Radiant Energy System is used to examine changes in ADRE since 2002 alongside changes in aerosol optical depth (AOD) from the Moderate Resolution Spectroradiometer. At global scales, neither ADRE nor AOD show a significant trend. Over the northern hemisphere (NH), ADRE increases by 0.18 ± 0.17 Wm⁻² per decade (less reflection to space) but shows no significant change over the southern hemisphere. The increase in the NH is primarily due to emission reductions in China, the United States, and Europe. The COVID‐19 shutdown shows no noticeable impact on either global ADRE or AOD, but there is a substantial influence over northeastern China in March 2020. In contrast, February 2020 anomalies in ADRE and AOD are within natural variability even though the impact of the shutdown on industry was more pronounced in February than March. The reason is because February 2020 was exceptionally hot and humid over China, which compensated for reduced emissions. After accounting for meteorology and normalizing by incident solar flux, February ADRE anomalies increase substantially, exceeding the climatological mean ADRE by 23%. February and March 2020 correspond to the only period in which adjusted anomalies exceed the 95% confidence interval for 2 consecutive months. Distinct water‐land differences over northeastern China are observed in ADRE but not in AOD. This is likely due to the influence of surface albedo on ADRE in the presence of absorbing aerosols.