Abstract A simple conceptual model of surface specific humidity change over land is described, based on the effect of increased moisture advection from the oceans in response to sea surface temperature (SST) warming. In this model, future q over land is determined by scaling the present-day pattern of land q by the fractional increase in the oceanic moisture source. Simple model estimates agree well with climate model projections of future (mean spatial correlation coefficient 0.87), so over both land and ocean can be viewed primarily as a thermodynamic process controlled by SST warming. Precipitation change is also affected by , and the new simple model can be included in a decomposition of tropical precipitation change, where it provides increased physical understanding of the processes that drive over land. Confidence in the thermodynamic part of extreme precipitation change over land is increased by this improved understanding, and this should scale approximately with Clausius–Clapeyron oceanic q increases under SST warming. Residuals of actual climate model from simple model estimates are often associated with regions of large circulation change, and can be thought of as the “dynamical” part of specific humidity change. The simple model is used to explore intermodel uncertainty in , and there are substantial contributions to uncertainty from both the thermodynamic (simple model) and dynamical (residual) terms. The largest cause of intermodel uncertainty within the thermodynamic term is uncertainty in the magnitude of global mean SST warming.
Iterative approach to calculating the Monin-Obukov length LStep 1. Calculate the stability parameter for f(zx/L) using the estimated or provided heights z of the measurements for x=T, x=q and x=u as described above:Step 2. Calculate the dimensionless profiles for f(zx/L) for x=T, x=q and x=u:If ζx < -0.01 (unstable):
Abstract. The International Surface Temperature Initiative (ISTI) is striving towards substantively improving our ability to robustly understand historical land surface air temperature change at all scales. A key recently completed first step has been collating all available records into a comprehensive open access, traceable and version-controlled databank. The crucial next step is to maximise the value of the collated data through a robust international framework of benchmarking and assessment for product intercomparison and uncertainty estimation. We focus on uncertainties arising from the presence of inhomogeneities in monthly mean land surface temperature data and the varied methodological choices made by various groups in building homogeneous temperature products. The central facet of the benchmarking process is the creation of global-scale synthetic analogues to the real-world database where both the "true" series and inhomogeneities are known (a luxury the real-world data do not afford us). Hence, algorithmic strengths and weaknesses can be meaningfully quantified and conditional inferences made about the real-world climate system. Here we discuss the necessary framework for developing an international homogenisation benchmarking system on the global scale for monthly mean temperatures. The value of this framework is critically dependent upon the number of groups taking part and so we strongly advocate involvement in the benchmarking exercise from as many data analyst groups as possible to make the best use of this substantial effort.
Abstract This paper describes a new homogenization algorithm validation methodology, and its use to assess the skill of eight different algorithms, when applied to synthetic daily temperature time series. These algorithms were ACMANT, Climatol (in both daily and monthly configurations), DAP, HOM, MAC‐D, MASH and SpliDHOM. Algorithms were tested on benchmark data replicating daily temperature variability in four regions in North America: Wyoming, the South East, the North East and the South West. These benchmarks contained plausible spatial and temporal correlation, differing station densities and both abrupt and gradual inhomogeneities. Algorithm ability was assessed both according to detection of inhomogeneities and correction of their effects, investigating bias, root‐mean‐square error (RMSE), linear trend recovery, station extremes and variability recovery. Inhomogeneities with a magnitude greater than 1°C were those most commonly detected with smaller inhomogeneities, and those that were not constant over time, proving harder to identify. Regional RMSE was always reduced by all algorithms and regional bias was reduced in over half of the region/scenario pairs. Trend recovery was variable, but the correct sign of regional trends was retained by all algorithms. Areas for future algorithm improvement include working with autocorrelated data and correcting moments higher than the mean. The data are available from https://www.metoffice.gov.uk/hadobs/benchmarks and the validation code from https://github.com/RachelKillick/Daily_benchmarks allowing the extension of this work and its application to new algorithms.
AbstractA simple conceptual model of surface specific humidity change over land is described, based on the effect of increased moisture advection from the oceans in response to sea surface temperature (SST) warming. In this model, future q over land is determined by scaling the present-day pattern of land q by the fractional increase in the oceanic moisture source. Simple model estimates agree well with climate model projections of future (mean spatial correlation coefficient 0.87), so over both land and ocean can be viewed primarily as a thermodynamic process controlled by SST warming. Precipitation change is also affected by , and the new simple model can be included in a decomposition of tropical precipitation change, where it provides increased physical understanding of the processes that drive over land. Confidence in the thermodynamic part of extreme precipitation change over land is increased by this improved understanding, and this should scale approximately with Clausius–Clapeyron oceanic q incre...
The Hadley Centre at the U.K. Met Office has created a global sub-daily dataset of several station-observed climatological variables which is derived from and is a subset of the NCDC's Integrated Surface Database. Stations were selected for inclusion into the dataset based on length of the data reporting period and the frequency with which observations were reported. The data were then passed through a suite of automated quality-control tests to remove bad data. See the HadISD web page [http://www.metoffice.gov.uk/hadobs/hadisd/] for more details and access to previous versions of the dataset.
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