The multi-scale structure of atmospheric energetic constraints on globally averaged precipitation

<p>This study presents a multi-scale analysis of cross-correlations based on Haar fluctuations of globally averaged anomalies of precipitation (<span class="inline-formula"><i>P</i></span>), precipitable water vapor (PWV), surface temperature (<span class="inline-formula"><i>T</i></span>), and atmospheric radiative fluxes. The results revealed an emergent transition between weak correlations at sub-yearly timescales (down to <span class="inline-formula">∼5</span> days) to strong correlations at timescales larger than about <span class="inline-formula">∼1</span>–2 years (up to <span class="inline-formula">∼1</span> decade). At multiyear timescales, (i) Clausius–Clapeyron becomes the dominant control of PWV (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow><msub><mi mathvariant="italic">ρ</mi><mrow><mi mathvariant="normal">PWV</mi><mo>,</mo><mi>T</mi></mrow></msub><mo>≈</mo><mn mathvariant="normal">0.9</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="63pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="24291660959f156841c7f7a6f0a2209d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="esd-10-219-2019-ie00001.svg" width="63pt" height="13pt" src="esd-10-219-2019-ie00001.png"/></svg:svg></span></span>), (ii) surface temperature averaged over global land and over global ocean (sea surface temperature, SST) become strongly correlated (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M7" display="inline" overflow="scroll" dspmath="mathml"><mrow><msub><mi mathvariant="italic">ρ</mi><mrow><mi mathvariant="normal">Tland</mi><mo>,</mo><mi mathvariant="normal">SST</mi></mrow></msub><mo>∼</mo><mn mathvariant="normal">0.6</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="75pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="d35b7260d9a732acc2b584a4b2ec5455"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="esd-10-219-2019-ie00002.svg" width="75pt" height="13pt" src="esd-10-219-2019-ie00002.png"/></svg:svg></span></span>); (iii) globally averaged precipitation variability is dominated by energetic constraints, specifically the surface downwelling longwave radiative flux (DLR) (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M8" display="inline" overflow="scroll" dspmath="mathml"><mrow><msub><mi mathvariant="italic">ρ</mi><mrow><mi mathvariant="normal">P</mi><mo>,</mo><mi mathvariant="normal">DLR</mi></mrow></msub><mo>≈</mo><mo>-</mo><mn mathvariant="normal">0.8</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="67pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="cae7d65134fe9eed3a7e2a3bbd287df4"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="esd-10-219-2019-ie00003.svg" width="67pt" height="13pt" src="esd-10-219-2019-ie00003.png"/></svg:svg></span></span>) displayed stronger correlations than the direct response to <span class="inline-formula"><i>T</i></span> fluctuations, and (iv) cloud effects are negligible for the energetic constraints in (iii), which are dominated by clear-sky DLR. At sub-yearly timescales, all correlations underlying these four results decrease abruptly towards negligible values. Such a transition has important implications for understanding and quantifying the climate sensitivity of the global hydrological cycle. The validity of the derived correlation structure is demonstrated by reconstructing global precipitation time series at 2-year resolution, relying on the emergent strong correlations (<span class="inline-formula"><i>P</i></span> vs. clear-sky DLR). Such a simple linear sensitivity model was able to reproduce observed <span class="inline-formula"><i>P</i></span> anomaly time series with similar accuracy to an (uncoupled) atmospheric model (ERA-20CM) and two climate reanalysis (ERA-20C and 20CR). The linear sensitivity breaks down at sub-yearly timescales, whereby the underlying correlations become negligible. Finally, the relevance of the multi-scale framework and its potential for stochastic downscaling applications are demonstrated by deriving accurate monthly <span class="inline-formula"><i>P</i></span> probability density functions (PDFs) from the reconstructed 2-year <span class="inline-formula"><i>P</i></span> time series based on scale-invariant arguments alone. The derived monthly PDFs outperform the statistics simulated by ERA-20C, 20CR, and ERA-20CM in reproducing observations.</p>