## 1 Introduction  
Solar-induced fluorescence (SIF) is a proxy of plant photosynthetic activity. When plants absorb a certain spectrum band of sunlight, some of the energy is emitted as fluorescence, which is detected as SIF. The first record of SIF was made almost two centuries ago by Sir [PERSON], who discovered that a beam of sunlight striking a green alcoholic extract of Laurel leaves elicited a brilliant red light [1, 2]. SIF can be retrieved from reflected radiance measured by satellite-borne instruments. Research has shown that SIF is correlated with gross primary productivity (GPP; e.g., [3, 4, 5, 6, 7, 8, 9, 10]). In particular, changes in SIF magnitude at wavelengths greater than 740 nm are shown to be sufficient for tracking photosynthetic dynamics [11]. Therefore, SIF is a useful variable for certain environmental applications, including indicating changes in vegetation in response to climate change and natural disasters.  
Significant advancements have been made in the last decades in the remote sensing observations of SIF and GPP. Such efforts were first made with the Greenhouse Gases Observing Satellite (GOSAT, [12, 13]), then with the Global Ozone Monitoring Experiment-2 (GOME-2), the Scanning Imaging Absorption Spectrometer for Atmospheric Chattorography (SCIAMACHY, [14]), and the Orbiting Carbon Observatory (OCO-2; [5]). Among these, GOME-2 is a European instrument deployed on the MetOp-A satellite. It was launched on October 19, 2006 to continue the long-term monitoring of atmospheric ozone started by GOME-2 on ERS-2 and SCIAMACHY on Envisat. Because different gases in the atmosphere absorb different wavelengths of light, the GOME-2 scanning spectrometer is designed to capture light reflected from the Earth's surface and atmosphere and use it to map concentrations of atmospheric ozone, nitrogen dioxide, sulfur dioxide, SIF, and other ultraviolet radiation. It has daily global coverage. Its nadir field of view on the ground is 80 km \(\times\) 40 km. As such, GOME-2 can make a significant contribution to climate research while providing near real-time data for use in SIF, GPP, and air quality research.  
The decade-long satellite-derived SIF has provided a good opportunity to explore interannual to decadal variations in SIF and GPP. Here, we hypothesize that all earth ecosystem variability, including GPP and its proxy SIF, are affected by climate variations. The main contribution of this study is to apply a global empirical orthogonal function (EOF) analysis of SIF to systematically quantify the relations between the large-scale GPP variability and climate variations.  
The EOF technique was chosen because it can separate the spatiotemporal signal into a sum of orthogonal modes by maximizing the variance explained by each mode. It has been widely used in oceanography and atmospheric research (e.g., [15; 16; 17]). We recognize that several very recent studies (e.g., [18; 19; 20; 21; 22; 23]) have successfully applied machine learning methods to generate additional higher spatial resolutions (e.g., 0.05\({}^{\circ}\)) and finer temporal resolution (e.g., 4-day) SIF reconstructions using GOME-2 and SCIAMACHY datasets. For our research purpose, we decided to use the original, GOME-2 0.5 degree, monthly SIF data because: (1) we tried to avoid possible complications associated with inter-sensor calibrations across different satellite sensor platforms [24]; and (2) our focus was on the large-scale signals connected with climate trends and variability rather than high-frequency, small-scale signals that are often driven by local-processes.  
This manuscript is organized as follows. Section 2 describes the datasets, climate indices, and the analytical method. Section 3 presents the global EOF analysis and relates the results to regional climate indices. We also compare and contrast SIF conditions in 2011 and 2015 to explore the impact of ENSO on the global SIF variability. Concluding remarks are given in Section 4.