HadISDH.land Update Document version 4.2.0.2019f

HadISDH.land update document covering the latest changes in version 4.2.0.2019f from previous versions

Investigation and quality assessment of the Past Weather Code from the Integrated Surface Database

Quantitative SYNOP Code weather variables such as rainfall amount, although of high societal and environmental importance, are frequently subject to recording errors and inhomogeneities resulting in uncertain conclusions. Here we assess the viability of the more qualitative Past Weather Code (PWC) for its use in robust climate analysis in the belief that it is less prone to both random and systematic errors. The Past Weather Code data, from a selection of the National Oceanographic and Atmospheric Administration’s Integrated Surface Database (ISD) (4731 sufficiently long stations), is quality assessed by searching for inhomogeneities in station PWC time series, removing the offending stations and averaging the remaining stations into a global gridded dataset. PWCs 6 (Rainfall), 7 (Snowfall) and 9 (Thunderstorms) are found to robustly exhibit seasonal features, e.g. the Indian monsoon and peak Northern Hemispheric winter snowfall. Precipitation responses to the North Atlantic Oscillation are also detected in winter PWC 6 data over Europe

HadISDH.marine Update Document version 1.1.0.2020f

Update notes for HadISDH.marine

HadISDH.land Update Document version 4.3.1.2020f

Update notes for HadISDH land

HadISD: a quality-controlled global synoptic report database for selected variables at long-term stations from 1973--2011

[Abridged] This paper describes the creation of HadISD: an automatically quality-controlled synoptic resolution dataset of temperature, dewpoint temperature, sea-level pressure, wind speed, wind direction and cloud cover from global weather stations for 1973--2011. The full dataset consists of over 6000 stations, with 3427 long-term stations deemed to have sufficient sampling and quality for climate applications requiring sub-daily resolution. As with other surface datasets, coverage is heavily skewed towards Northern Hemisphere mid-latitudes. The dataset is constructed from a large pre-existing ASCII flatfile data bank that represents over a decade of substantial effort at data retrieval, reformatting and provision. These raw data have had varying levels of quality control applied to them by individual data providers. The work proceeded in several steps: merging stations with multiple reporting identifiers; reformatting to netCDF; quality control; and then filtering to form a final dataset. Particular attention has been paid to maintaining true extreme values where possible within an automated, objective process. Detailed validation has been performed on a subset of global stations and also on UK data using known extreme events to help finalise the QC tests. Further validation was performed on a selection of extreme events world-wide (Hurricane Katrina in 2005, the cold snap in Alaska in 1989 and heat waves in SE Australia in 2009). Although the filtering has removed the poorest station records, no attempt has been made to homogenise the data thus far. Hence non-climatic, time-varying errors may still exist in many of the individual station records and care is needed in inferring long-term trends from these data. A version-control system has been constructed for this dataset to allow for the clear documentation of any updates and corrections in the future.Comment: Published in Climate of the Past, www.clim-past.net/8/1649/2012/. 31 pages, 23 figures, 9 pages. For data see http://www.metoffice.gov.uk/hadobs/hadis

Development of the HadISDH.marine humidity climate monitoring dataset

Atmospheric humidity plays an important role in climate analyses. Here we describe the production and key characteristics of a new quasi-global marine humidity product intended for climate monitoring, HadISDH.marine. It is an in situ multivariable marine humidity product, gridded monthly at a 5∘×5∘ spatial resolution from January 1973 to December 2018 with annual updates planned. Currently, only reanalyses provide up-to-date estimates of marine surface humidity, but there are concerns over their long-term stability. As a result, this new product makes a valuable addition to the climate record and will help address some of the uncertainties around recent changes (e.g. contrasting land and sea trends, relative-humidity drying). Efforts have been made to quality-control the data, ensure spatial and temporal homogeneity as far as possible, adjust for known biases in non-aspirated instruments and ship heights, and also estimate uncertainty in the data. Uncertainty estimates for whole-number reporting and for other measurement errors have not been quantified before for marine humidity. This is a companion product to HadISDH.land, which, when combined, will provide methodologically consistent land and marine estimates of surface humidity. The spatial coverage of HadISDH.marine is good over the Northern Hemisphere outside of the high latitudes but poor over the Southern Hemisphere, especially south of 20∘ S. The trends and variability shown are in line with overall signals of increasing moisture and warmth over oceans from theoretical expectations and other products. Uncertainty in the global average is larger over periods where digital ship metadata are fewer or unavailable but not large enough to cast doubt over trends in specific humidity or air temperature. Hence, we conclude that HadISDH.marine is a useful contribution to our understanding of climate change. However, we note that our ability to monitor surface humidity with any degree of confidence depends on the continued availability of ship data and provision of digitized metadata

Challenges in quantifying changes in the global water cycle

Human influences have likely already impacted the large-scale water cycle but natural variability and observational uncertainty are substantial. It is essential to maintain and improve observational capabilities to better characterize changes. Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time-series over land but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols, and due to large climate variability presently limits confidence in attribution of observed changes

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

Implications of climate change for agricultural productivity in the early twenty-first century

This paper reviews recent literature concerning a wide range of processes through which climate change could potentially impact global-scale agricultural productivity, and presents projections of changes in relevant meteorological, hydrological and plant physiological quantities from a climate model ensemble to illustrate key areas of uncertainty. Few global-scale assessments have been carried out, and these are limited in their ability to capture the uncertainty in climate projections, and omit potentially important aspects such as extreme events and changes in pests and diseases. There is a lack of clarity on how climate change impacts on drought are best quantified from an agricultural perspective, with different metrics giving very different impressions of future risk. The dependence of some regional agriculture on remote rainfall, snowmelt and glaciers adds to the complexity. Indirect impacts via sea-level rise, storms and diseases have not been quantified. Perhaps most seriously, there is high uncertainty in the extent to which the direct effects of CO2 rise on plant physiology will interact with climate change in affecting productivity. At present, the aggregate impacts of climate change on global-scale agricultural productivity cannot be reliably quantified

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes