Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

MetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

Multi-sensor data merging of sea ice concentration and thickness

With the rapid change in the Arctic sea ice,a large number of sea ice observations have been collected in recent years,and it is expected that an even larger number of such observations will emerge in the coming years.To make the best use of these observations,in this paper we develop a multi-sensor optimal data merging(MODM)method to merge any number of different sea ice observations.Since such merged data are independent on model forecast,they are valid for model initialization and model validation.Based on the maximum likelihood estimation theory,we prove that any model assimilated with the merged data is equivalent to assimilating the original multi-sensor data.This greatly facilitates sea ice data assimilation,particularly for operational forecast with limited computational resources.We apply the MODM method to merge sea ice concentration(SIC)and sea ice thickness(SIT),respectively,in the Arctic.For SIC merging,the Special Sensor Microwave Imager/Sounder(SSMIS)and Advanced Microwave Scanning Radiometer 2(AMSR2)data are merged together with the Norwegian Ice Service ice chart.This substantially reduces the uncertainties at the ice edge and in the coastal areas.For SIT merging,the daily Soil Moisture and Ocean Salinity(SMOS)data is merged with the weekly-mean merged CryoSat-2 and SMOS(CS2SMOS)data.This generates a new daily CS2SMOS SIT data with better spatial coverage for the whole Arctic.

Erroneous use of Statistics behind Claims of a Major Solar Role in Recent Warming

In a study that attempted to relate solar and human activity to Earth's recent temperature change,Connolly et al.committed a basic error in the choice of statistical methods and so overreported the effect of the Sun.A major theme of their study was that there are many data sets of past solar activity,and some of these allegedly provide statistical evidence of“most of the recent global warming being due to changes in solar activity.”We avoid methods that are known to give inaccurate results and show that for 1970–2005 Northern Hemisphere land the corrected solar attribution fraction is-7%to+5%,compared with values of up to 64%reported in Connolly et al.Their higher values are entirely due to mistaken application of statistics.Unfortunately,we cannot test truly“recent”global warming since most of their solar data sets end before 2015,and two finish in the 1990s,but all tested post-1970 periods show similarly small solar contributions.The solar-climate linkage is an area of fascinating and ongoing research with rigorous technical discussion.We argue that instead of repeating errors,they should be acknowledged and corrected so that the debate can focus on areas of legitimate scientific uncertainty.

Relative Impacts of Sea Ice Loss and Atmospheric Internal Variability on the Winter Arctic to East Asian Surface Air Temperature Based on Large-Ensemble Simulations with NorESM2

To quantify the relative contributions of Arctic sea ice and unforced atmospheric internal variability to the “warm Arctic, cold East Asia”(WACE) teleconnection, this study analyses three sets of large-ensemble simulations carried out by the Norwegian Earth System Model with a coupled atmosphere–land surface model, forced by seasonal sea ice conditions from preindustrial, present-day, and future periods. Each ensemble member within the same set uses the same forcing but with small perturbations to the atmospheric initial state. Hence, the difference between the present-day(or future) ensemble mean and the preindustrial ensemble mean provides the ice-loss-induced response, while the difference of the individual members within the present-day(or future) set is the effect of atmospheric internal variability. Results indicate that both present-day and future sea ice loss can force a negative phase of the Arctic Oscillation with a WACE pattern in winter. The magnitude of ice-induced Arctic warming is over four(ten) times larger than the ice-induced East Asian cooling in the present-day(future) experiment;the latter having a magnitude that is about 30% of the observed cooling. Sea ice loss contributes about 60%(80%) to the Arctic winter warming in the present-day(future) experiment. Atmospheric internal variability can also induce a WACE pattern with comparable magnitudes between the Arctic and East Asia. Ice-lossinduced East Asian cooling can easily be masked by atmospheric internal variability effects because random atmospheric internal variability may induce a larger magnitude warming. The observed WACE pattern occurs as a result of both Arctic sea ice loss and atmospheric internal variability, with the former dominating Arctic warming and the latter dominating East Asian cooling.

The evolution of a geoscience standard:An instructive tale of science keyword development and adoption

In 1987,NASA sponsored an international workshop that inspired the Directory Interchange Format or DIF–a metadata format to enable“catalog interoperability”.The DIF formed the basis of the International Directory Network(IDN)and the Global Change Master Directory(GCMD)and included a set of science keywords.The primary intent was to catalog NASA Earth science and related data,but the keywords have been implemented in many different systems and adopted in varying ways by many different organizations around the world.This review provides an ethnographic examination of how the keywords have evolved and been managed and how they have been adopted over the last 35 years.It illustrates how semantic approaches have evolved over time and provides insights on how standards and associated processes can be sustained and adaptable.Ongoing institutional commitment is essential,but so is transparency and technical flexibility.Understanding and empowering the different roles involved in standards creation,maintenance,and use of standards as well as the services that standards enable is also critical.It is apparent that semantic representations need to be mindful of different contexts and carefully define verbs as well as nouns and categories.Understanding and representing relationships is central to interdisciplinary interoperability.