2023 Update from HadISDH.marine.1.6.0.2023f

1) How does the new version compare?

v1.6.0.2023f is virtually identical to v1.5.0.2022f in both coverage, large scale mean timeseries, and decadal trends. This bug fix does not change the overall story that we conclude from HadISDH.marine. This is that the amount of water vapour near the surface over oceans is increasing - the specific humidity (q) is increasing. Simultaneously, the relative humidity (RH) is still apparently decreasing, meaning that the air is apparently becoming less saturated. 

The the long-term trends in q for v1.6.0.2023f compared to v1.5.0.2022f are identical for the Globe and Northern Hemisphere and 0.01 g kg ^-1 decade ^-1 larger for the Tropics and Southern Hemisphere (Figs. 1f to i). 2023 shows a peak in marine q that is extraordinarily large compared to the historical record. Across the globe (Figure 1a) almost all gridboxes show trends in the same direction in both versions (99.1%  of gridboxes - Figure 1b), with 91.5 % showing moistening trends and 7.6% showing drying trends.

The the long-term trends in RH for v1.6.0.2023f compared to v1.5.0.2023f are identical for the Globe and Tropics. Over the Northern and Southern Hemispheres the decreasing trends in RH are slightly (0.01 %rh decade ^-1) weaker (less negative) for v1.6.0.2023f (Figs. 2f to i). Across the globe (Figure 2a) most gridboxes show trends in the same direction in both versions (96.8%  of gridboxes - Figure 2b), with 62 % showing decreasing saturation trends and 34.8 % showing increasing saturation trends.

As noted for previous versions, the decrease in RH over oceans remains an uncertain conclusion as it does not reconcile with models or theory. Conceivably, there may be regions where relatively warmer and dry air may be advected from the land which could locally lower RH, or regions where wind speed changes or differing trends in SST vs MAT might have the effect of lowering RH. Ultimately, spatial coverage of HadISDH.marine remains limited, with very little representation over the Southern Hemisphere and so the trends in q and RH may not be truly representative of the global trend. However, the increasing specific humidity is consistent with both climate models and theoretical expectation.

Figure 1. Difference in regional timeseries and decadal trends between HadISDH.marineq v1.6.0.2023f and v1.5.0.2022f. a) Ratio of v1.6.0.2023f to v1.5.0.2022f decadal trends (1973-2023 and 2022 respectively) with change in number of gridboxes (with at least 70% temporal completeness) annotated and identified by yellow (gained) and pink (lost). b) Scatter plot of gridbox trends with percentage in each quadrant of positive/positive, positive/negative, negative/negative and negative/positive annotated. c) Distribution of gridbox decadal trends for each version with mean and standard deviation annotated. d) Total gridbox coverage by 5 degree latitude band for each year for v1.6.0.2023f. e) Difference in total gridbox coverage by 5 degree latitude band for each year between v1.6.0.2023f and v1.5.0.2022f. f to i) regional mean monthly time series and decadal trends with 90th percentile confidence range annotated.

Figure 2. Difference in regional timeseries and decadal trends between HadISDH.marineRH v1.6.0.2023f and v1.5.0.2022f. a) Ratio of v1.6.0.2023f to v1.5.0.2022f decadal trends (1973-20223 and 2022 respectively) with change in number of gridboxes (with at least 70% temporal completeness) annotated and identified by yellow (gained) and pink (lost). b) Scatter plot of gridbox trends with percentage in each quadrant of positive/positive, positive/negative, negative/negative and negative/positive annotated. c) Distribution of gridbox decadal trends for each version with mean and standard deviation annotated. d) Total gridbox coverage by 5 degree latitude band for each year for v1.6.0.2023f. e) Difference in total gridbox coverage by 5 degree latitude band for each year between v1.6.0.2023f and v1.5.0.2022f. f to i) regional mean monthly time series and decadal trends with 90th percentile confidence range annotated.

2) What's New?

Although only last year we implemented a new algorithm (Stull, 2011) to remove the large errors at high temperature/low humidity in the previous Jensen et al., (1990) method, a new method is now available which reduces moist bias errors at high temperature/low humidity (reaching +1.3 °C) and dry bias errors at low temperature/high humidity (approaching -1 °C). The new Non-iterative Evaluation of Wet-bulb Temperature (NEWT) method has been developed by Rob Warren at the Bureau of Meteorology and coded in python. Its errors are far smaller at ± 0.01 °C. Rogers and Warren (2024) introduce the new method and compare it against the Stull (2011) and other methods (https://essopenarchive.org/users/714325/articles/698601-fast-and-accurate-calculation-of-wet-bulb-temperature-for-humid-heat-extremes). Here we use their python code to calculate adiabatic wet-bulb temperatures using polynomial fits from surface pressure, air temperature and specific humidity. This change only affects the wet-bulb temperature fields and differences are negligible in large-scale means and far less than 1 °C for the most part. Differences are largest over hot/dry and cold/humid air conditions. We still use the Stull (2011) method to decide whether to calculate vapour pressure with respect to ice or water because it is faster to implement so there is no change to other variables. 

The only other change is an additional 12 months of data.

3) Summary of changes by level of technicality.

MAJOR CHANGES (X): 

• none

MINOR CHANGES (Y): 

• Change of wet-bulb temperature formula from Stull (2011) to the Non-iterative Evaluation of Wet-bulb Temperature (NEWT) method - https://essopenarchive.org/users/714325/articles/698601-fast-and-accurate-calculation-of-wet-bulb-temperature-for-humid-heat-extremes.

BUG FIXES AND HISTORICAL DATA UPDATES (Z): 

• None