A mathematical analysis of the divergence problem in dendroclimatology

Craig Loehle has a new paper in the journal Climatic Change, which examines the divergence problem in paleoclimate reconstructions using tree rings.

The Abstract states:

Tree rings provide a primary data source for reconstructing past climates, particularly over the past 1,000 years. However, divergence has been observed in twentieth century reconstructions. Divergence occurs when trees show a positive response to warming in the calibration period but a lesser or even negative response in recent decades. The mathematical implications of divergence for reconstructing climate are explored in this study. Divergence results either because of some unique environmental factor in recent decades, because trees reach an asymptotic maximum growth rate at some temperature, or because higher temperatures reduce tree growth. If trees show a nonlinear growth response, the result is to potentially truncate any historical temperatures higher than those in the calibration period, as well as to reduce the mean and range of reconstructed values compared to actual. This produces the divergence effect. This creates a cold bias in the reconstructed record and makes it impossible to make any statements about how warm recent decades are compared to historical periods. Some suggestions are made to overcome these problems.

In conclusion:

the nonlinear response of trees to temperature explains the divergence problem, including cases where divergence was not found. The analysis here also shows why non-tree ring proxies often show the Medieval Warm Period but tree ring-based reconstructions more often do not. While Fritts (1976) notes the parabolic tree growth response to temperature, recent discussions of the divergence problem have not focused on this mechanism and climate reconstructions continue to be done using a linear response model. When the divergence problem clearly indicates that the linearity assumption is questionable, it is not good practice to carry on as if linearity is an established fact.

New Paper: US Hurricane Counts are Significantly Related to Solar Activity

There is a new GRL paper in press by Elsner and Jagger entitled: 'United States and Caribbean tropical cyclone activity related to the solar cycle.'

The Abstract states:

The authors report on a finding that annual U.S hurricane counts are significantly related to solar activity. The relationship results from fewer intense tropical cyclones over the Caribbean and Gulf of Mexico when sunspot numbers are high. The finding is in accord with the heat-engine theory of hurricanes that predicts a reduction in the maximum potential intensity with
a warming in the layer near the top of the hurricane. An active sun warms the lower stratosphere and upper troposphere through ozone absorption of additional ultraviolet (UV) radiation. Since the dissipation of the hurricane’s energy occurs through ocean mixing and atmospheric transport, tropical cyclones can act to amplify the effect of relatively small changes in the sun’s output thereby appreciably altering the climate. Results have implications for life and property throughout the Caribbean, Mexico, and portions of the United States.

New Climate Paper Provides Evidence for Negative Feedback

A new paper by Roy Spencer and William Braswell is now available as an early online release in the Journal of Climate. The paper is entitled: 'Potential Biases in Feedback Diagnosis from Observational Data: A Simple Model Demonstration.'

The Abstract states:

Feedbacks are widely considered to be the largest source of uncertainty in determining the sensitivity of the climate system to increasing anthropogenic greenhouse gas concentrations, yet our ability to diagnose them from observations has remained controversial. Here we use a simple model to demonstrate that any non-feedback source of top-of-atmosphere radiative flux variations can cause temperature variability which then results in a positive bias in diagnosed feedbacks. We demonstrate this effect with daily random flux variations, as might be caused by stochastic fluctuations in low cloud cover. The daily noise in radiative flux then causes interannual and decadal temperature variations in the model's 50 m deep swamp ocean. The amount of bias in the feedbacks diagnosed from time-averaged model output depends upon the size of the non-feedback flux variability relative to the surface temperature variability, as well as the sign and magnitude of the specified (true) feedback. For model runs producing monthly shortwave flux anomaly and temperature anomaly statistics similar to those measured by satellites, the diagnosed feedbacks have positive biases generally in the range of −0.3 to −0.8 W m−2 K−1. These results suggest that current observational diagnoses of cloud feedback – and possibly other feedbacks -- could be significantly biased in the positive direction.